Managing data objects for graph-based data structures

ABSTRACT

Various embodiments provide methods, systems, apparatus, computer program products, and/or the like for managing, ingesting, monitoring, updating, and/or extracting/retrieving information/data associated with an electronic record (ER) stored in an ER data store and/or accessing information/data from the ER data store, wherein the ERs are generated, updated/modified, and/or accessed via a graph-based domain ontology.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Application No. 62/828,517filed Apr. 3, 2019; U.S. Application No. 62/828,526 filed Apr. 3, 2019;U.S. Application No. 62/845,084 filed May 8, 2019; U.S. Application No.62/845,109 filed May 8, 2019; U.S. Application No. 62/845,085 filed May8, 2019; U.S. Application No. 62/845,089 filed May 8, 2019; U.S.Application No. 62/860,031 filed Jun. 11, 2019; U.S. Application No.62/860,047 filed Jun. 11, 2019; U.S. Application No. 62/860,050 filedJun. 11, 2019; U.S. Application No. 62/873,217 filed Jul. 12, 2019; andU.S. Application No. 62/874,638 filed Jul. 16, 2019, the contents ofwhich are hereby incorporated by reference in their entireties.

TECHNICAL FIELD

Various embodiments relate to the management, updating, and/orretrieving of information/data from an electronic record (ER) data storebased at least in part on a graph-based domain ontology. Variousembodiments relate to various technical solutions for technicalchallenges with regard to the managing, ingesting, monitoring, updating,and/or extracting/retrieving of information/data from an ER data storestoring ERs comprising a plurality of single best record objects(SBROs).

BACKGROUND

Various systems for managing information/data (e.g., medical and/orhealth information/data) are known. However, in the healthcare context,for example, various technical challenges prevent current systems frombeing patient-centric and/or patient-focused. For example, a singlehealthcare event may result in tens of messages (e.g., thirty messages)being generated/created by various systems (e.g., an ER system, a clinicsystem, a claims processing system, and/or the like). However,traditional systems for managing, ingesting, monitoring, updating,and/or extracting/retrieving such information/data fail to streamlinethese tens of messages into a single set of information/datacorresponding the health event. Further, traditional systems fail toprovide computationally efficient and accurate processes that reduce thestrain on system resources and provide a simple user experience thatcombines several sets of information/data (which may be inconsistent) ina transparent manner.

BRIEF SUMMARY OF SOME EXAMPLE EMBODIMENTS

Various embodiments provide methods, systems, apparatus, computerprogram products, and/or the like for managing, ingesting, monitoring,updating, and/or extracting/retrieving information/data associated withan ER data store and/or accessing information/data from the ER datastore, wherein the ERs are generated, updated/modified, and/or accessedvia a graph-based domain ontology. In various embodiments, a graph-baseddomain ontology defines a plurality of concepts corresponding to thedomain and the relationships between and among the concepts. Theexamples described herein generally relate to the healthcare domain;however, various embodiments of the present invention may relate toand/or be used in a variety of domains.

In various embodiments, the disclosed system comprises a uniquegraph-based healthcare ontology to overcome limitations of the prior artin representing knowledge and information/data in a uniform and unifyingway of dealing with health information/data. Most prior art systemscomprise some type of coding scheme, internal or external. Some of themore popular schemes include the Standardized Nomenclature of Medicine(SNOMED), and ICD-9 or ICD-10 (the International Classification ofDisease, including the clinical modification variations). While thesecoding schemes have long been proposed as needed for health IT systems,and have been adopted and used by many systems, they have been usedprimarily in retrospective studies, and have not had the desired impacton real-time information/data delivery.

In accordance with one aspect, methods, systems, apparatus, computerprogram products, and/or the like are provided. In one embodiment, thesemay include receiving an observable packet data object or a dataartifact packet data object, wherein (a) the data artifact packet dataobject is generated based at least in part on the observable packet dataobject, (b) the observable packet data object is an XML documentgenerated based at least in part on parsing a message received from asource system, (c) the data artifact packet data object comprises anentity identifier identifying a subject entity and a pluralityobservable-value pairs, and (d) each observable in a correspondingobservable-value pair is identifiable by an ontology concept identifierdefined by a graph-based domain ontology; automatically generating acontainer tree data structure comprising a data artifact packetcontainer node as a root node, wherein (a) the container tree datastructure is generated based at least in part on the data artifactpacket data object, (b) the container tree data structure comprises aplurality of container nodes that are descendants of the root node basedat least in part on the data artifact packet data object, and (c) eachcontainer node of the plurality of container nodes corresponds to onepair of the plurality of observable-value pairs; automaticallyidentifying an entity associated with the container tree data structurebased at least in part on the entity identifier; automaticallytraversing the container tree data structure in a depth-first traversalto generate a data transfer object for each of container node of theplurality of container nodes, wherein each data transfer objectcorresponds to one pair of the plurality of observable-value pairs; andautomatically providing at least one of the plurality of data transferobjects for use in performing a database update function.

In various embodiment, these may further include, wherein a value of atleast one observable-value pair of the plurality observable-value pairscomprises at least one of a source vocabulary description, a sourcevocabulary code, or a text description of an observable; wherein, duringthe depth-first traversal of the container tree data structure, anaggregator method resolves the at least one of the source vocabularydescription, the source vocabulary code, or the text description of theobservable; and wherein a hierarchy of container nodes of the containertree is determined based at least in part on the graph-based domainontology.

In additional embodiments, these may yet include, wherein automaticallygenerating the container tree data structure comprises: determining atype of container node for an observable-value pair of the plurality ofobservable-value pairs based at least in part on the graph-based domainontology; determining whether a container node having the determinedtype is present in the container tree data structure; and responsive todetermining that a container node having the determined type is presentin the container tree data structure, storing the observable-value pairin the container node.

In some embodiments, these may include, wherein automatically generatingthe container tree data structure comprises: determining a type ofcontainer node for an observable-value pair of the plurality ofobservable-value pairs based at least in part on the graph-based domainontology; determining whether a container node having the determinedtype is present in the container tree data structure; and responsive todetermining that a container node having the determined type is notpresent in the container tree data structure: (a) constructing thecontainer node having the determine type, (b) inserting the containernode into an appropriate position in the container tree data structure,wherein the appropriate position in the container tree data structure isdetermined based at least in part on the graph-based domain ontology,and (c) storing the observable-value pair in the container node.

In particular embodiments, these may also include, wherein thedepth-first traversal of the container tree data structure comprisescomparing a value of an observable-value pair of the plurality ofobservable-value pairs to at least one requirement corresponding to anobservable of the observable-value pair to determine whether the valuesatisfies the at least one requirement; and further comprise determininga confidence score for each of the plurality of observable-value pairs.

In accordance with one aspect, methods, systems, apparatus, computerprogram products, and/or the like are provided. In one embodiment, thesemay include automatically receiving an extractable packet data object,wherein (a) the extractable packet data object is an XML, document, (a)a data artifact packet data object is generated based at least in parton the extractable packet data object, (b) the data artifact packet dataobject comprises an entity identifier identifying a subject entity, (c)the data artifact packet data object comprises one or more ontologyconcept identifiers corresponding respectively to one or more conceptsdefined within a graph-based domain ontology, and (d) the graph-baseddomain ontology comprises a specific set or hierarchy of concepts andrelationships among those concepts related to a domain; automaticallygenerating a container tree data structure comprising a data artifactcontainer node as the root node based at least in part on the dataartifact packet data object, wherein (a) the container tree datastructure comprises a plurality of container nodes that are descendantsof the root node based at least in part on the data artifact packet dataobject, (b) each container node of plurality of container nodescomprises an observable and an empty value for the correspondingobservable, (c) each empty value is to be retrieved from a database oraggregated from retrieved empty values; automatically traversing each ofthe plurality of container nodes of the container tree data structure ina depth-first traversal, wherein (a) at each container node that is aleaf node in the traversal, a method is executed to retrieve a non-emptyvalue from the database for the corresponding observable, and (b) at thecompletion of the traversal, each of the plurality of container nodescomprises a non-empty value for the corresponding observable; after thedepth-first traversal, automatically processing the container tree datastructure to generate at least one observable group, wherein the atleast one observable group comprises each observable and thecorresponding non-empty value; and generating, based at least in part onthe observable groups, an information message comprising the observablegroup.

In various embodiment, these may further include, wherein a hierarchy ofcontainer nodes of the container tree data structure is determined basedat least in part on the graph-based domain ontology.

In a particular embodiment, these may also include, whereinautomatically generating the container tree data structure comprises:determining a type of container node that should contain an observablecorresponding to an ontology concept identifier in the data artifactpacket data object; determining whether a container node having thedetermined type is present in the container tree data structure; andresponsive to determining that a container node having the determinedtype is present in the container tree data structure, storing theobservable and a corresponding empty value in the container node.

In yet another embodiment, these may also include, wherein automaticallygenerating the container tree data structure comprises: determining atype of container node that should contain an observable correspondingto an ontology concept identifier in the data artifact packet dataobject; determining whether a container node having the determined typeis present in the container tree data structure; and responsive todetermining that a container node having the determined type is notpresent in the container tree data structure: (a) constructing thecontainer node having the determine type, (b) storing the observable anda corresponding empty value in the container node, and inserting thecontainer node into an appropriate position in the container tree datastructure, wherein the appropriate position in the container tree datastructure is determined based at least in part on the graph-based domainontology.

In still another embodiment, these also include, wherein the depth-firsttraversal of the container tree data structure comprises aggregating twoor more values of a subcontainer node to generate a value of containercomprising the subcontainer; wherein the requested information messageis configured to be provided, at least in part, via a portlet for userconsumption; and wherein the depth-first traversal of the container treedata structure comprises retrieving the originating source vocabularycorresponding to the values.

And in one embodiment, these include retrieving a confidence scorecorresponding to at least a portion of an observable group from thedatabase, wherein the information message comprises the confidencescore.

In accordance with one aspect, methods, systems, apparatus, computerprogram products, and/or the like are provided. In one embodiment, thesemay include automatically receiving a plurality of messages via one ormore communications interfaces, each message comprising datacorresponding to a subject entity; for each message of the plurality ofmessages, generating an application programming interface (API) requestto an identity matching service, wherein the API request comprises afirst attribute and a second attribute; for each message of theplurality of messages, receiving an API response comprising an entityidentifier corresponding to the subject entity, for each message of theplurality of messages, automatically generating a data artifact packetdata object, wherein (a) the data artifact packet data object isgenerated based at least in part on an observable packet data object,and (b) the observable packet data object is an XML, document generatedbased at least in part on parsing a message received from a sourcesystem; automatically storing each data artifact packet data object in adatabase, wherein each data artifact packet data object is identifiableby the corresponding entity identifier; automatically detecting a userinteraction event corresponding to a first entity identifier; responsiveto detecting the user interaction event corresponding to the firstentity identifier, automatically retrieving a plurality of data artifactpacket data objects corresponding to the first entity identifier; andproviding each of the plurality of data artifact packet data objectscorresponding to the first entity identifier to an ingestion processingmodule for processing.

In various embodiments, these may also include, wherein (a) the userinteraction event authenticates a user by a portal via which the usermay access data corresponding to the first entity identifier and (b) theuser is an owner corresponding to the first entity identifier; whereinthe one or more relationships are identified based at least in part on agraph data structure (e.g., relationship graph) based at least in parton the graph-based domain ontology; wherein the user interaction eventis detected based at least in part on historical behavior of one or moreusers having access to data corresponding to the first entityidentifier; wherein (a) the user interaction event authenticates a userby a portal via which the user may access data corresponding to thefirst entity identifier and (b) the user has a relationship definedwithin a graph-based domain ontology with an entity that is an ownercorresponding to the first entity identifier; wherein the ingestionprocessing module processes each of the plurality of data artifactpacket data objects corresponding to the first entity identifier togenerate data transfer objects for use in performing a database updatefunction.

In a particular embodiment, these also include, wherein detection of theuser interaction event comprises: determining that the user has beenauthenticated by the portal; identifying one or more relationships ofwhich the user is a participant based at least in part on an entityidentifier corresponding to the user; and determining, based at least inpart on the one or more relationships, that the user has access to datacorresponding to the first entity identifier.

In yet another embodiment, these may further include for each of theplurality of data artifact packet data objects: automatically generatinga container tree data structure comprising a data artifact packetcontainer node as a root node, wherein (a) the container tree datastructure is generated based at least in part on the data artifactpacket data object, (b) the container tree data structure comprises aplurality of container nodes that are descendants of the root node basedat least in part on the data artifact packet data object, and (c) eachcontainer node of the plurality of container nodes corresponds to onepair of a plurality of observable-value pairs; automatically traversingthe container tree data structure in a depth-first traversal to generatea data transfer object for each of container node of the plurality ofcontainer nodes, wherein each data transfer object corresponds to onepair of the plurality of observable-value pairs; and automaticallyproviding at least one of the plurality of data transfer objects for usein performing a database update function.

In still another embodiment, these may also include, whereinautomatically generating the container tree data structure comprises:determining a type of container node for an observable-value pair of theplurality of observable-value pairs based at least in part on agraph-based domain ontology; determining whether a container node havingthe determined type is present in the container tree data structure; andresponsive to determining that a container node having the determinedtype is present in the container tree data structure, storing theobservable-value pair in the container node.

And in still another embodiment, these may further include, whereinautomatically generating the container tree data structure comprises:determining a type of container node for an observable-value pair of theplurality of observable-value pairs based at least in part on agraph-based domain ontology; determining whether a container node havingthe determined type is present in the container tree data structure; andresponsive to determining that a container node having the determinedtype is not present in the container tree data structure: (a)constructing the container node having the determine type, (b) insertingthe container node into an appropriate position in the container treedata structure, wherein the appropriate position in the container treedata structure is determined based at least in part on the graph-baseddomain ontology, and (c) storing the observable-value pair in thecontainer node.

In accordance with one aspect, methods, systems, apparatus, computerprogram products, and/or the like are provided. In one embodiment, thesemay include receiving, by a data store management layer in communicationwith a primary program, a request, wherein the request is to (a) load aprimary software object instance from a database, or (b) persist aprimary software object instance in the database; identifying, by thedata store management layer, one or more database objects correspondingto the primary software object; determining, by a persistence manager ofthe data store management layer, a mapping of the one or more databaseobjects to storage locations within the database based at least in parton the one or more database objects, wherein (a) the data storemanagement layer comprises a plurality of persistence managers 248, and(b) the persistence manager is associated with an entity class for theprimary software object; and automatically generating, by thepersistence manager of the data store management layer and based atleast in part on the determined mapping, an executable code portionconfigured to cause (a) a load request to be performed, or (b) a writerequest to be performed; executing, by the data store management layer,the executable code portion to cause: (a) in an instance of the loadrequest, one or more database objects stored at the storage locationswithin the database to be loaded as a functional primary software objectinstance, or (b) in an instance of the write request, one or more valuescorresponding to the primary software object instance to be written tothe database at the storage locations.

In one embodiment, these may also include executing, by the data storemanagement layer, a connect method to connect to the database; andexecuting, by the data store management layer, a disconnect method todisconnect from the database.

In yet another embodiment, these may further include, wherein theconnect and disconnect methods comprise at least one of (a) opening asession and closing the session or (b) opening a transaction within asession and closing the transaction within the session; wherein (a) theprimary program is database agnostic, and (b) the executable codeportion is an SQL statement; and wherein (a) the request to load theprimary software object instance from the database was generated andprovided by one of a rules engine or an extraction processing module, or(b) the request to persist the primary software object instance in thedatabase was generated and provided by a single best record objectprocess.

In a particular embodiment, these may also include updating a change logbased at least in part on (a) the loading of the one or more databaseobjects from the storage locations, or (b) the writing of the one ormore values corresponding to the primary software object instance at thestorage locations; returning the functional primary software objectinstance to the primary program; and generating a data artifact packetdata object based at least in part on the extractable packet dataobject, wherein the data artifact data object (a) comprises a subjectentity identifier identifying a subject entity, and (b) one or moreontology concept identifiers corresponding respectively to one or moreconcepts defined within a graph-based domain ontology.

In still another embodiment, these may include, wherein (a) at least aportion of the executable code portion was automatically inserted into adatabase object class definition corresponding to a class associatedwith at least one of the one or more database objects, and (b)generating the executable code portion comprises accessing the insertedexecutable code portion and populating one or more entity identifierstherein; wherein: (a) in an instance of the load request, loading theone or more database objects stored at the storage locations within thedatabase as a functional primary software object instance comprisesperforming a deserialization function on the one or more databaseobjects stored at the storage locations, or (b) in an instance of thewrite request, writing the one or more values corresponding to theprimary software object instance to the database at the storagelocations comprises performing a serialization function on the primarysoftware object instance; and wherein, in an instance of the loadrequest, the request is received from an extractable packet data object.

In still another embodiment, these may also include automaticallygenerating a container tree data structure comprising a data artifactcontainer node as the root node based at least in part on the dataartifact packet data object, wherein (a) the container tree datastructure comprises a plurality of container nodes that are descendantsof the root node based at least in part on the data artifact packet dataobject, (b) each container node of plurality of container nodescomprises an observable and an empty value for the correspondingobservable, (c) each empty value is to be retrieved from a database oraggregated from retrieved empty values; automatically traversing each ofthe plurality of container nodes of the container tree data structure ina depth-first traversal, wherein (a) at each container node that is aleaf node in the traversal, a method is executed to retrieve a non-emptyvalue from the database for the corresponding observable, and (b) at thecompletion of the traversal, each of the plurality of container nodescomprises a non-empty value for the corresponding observable; and afterthe depth-first traversal, automatically processing the container treedata structure to generate at least one observable group, wherein the atleast one observable group comprises each observable and thecorresponding non-empty value.

In a particular embodiment, these may further include, wherein, in aninstance of the write request, the request is received from anobservable packet data object and a data artifact packet data object isgenerated based at least in part on the observable packet data object,wherein the data artifact data object (a) comprises a subject entityidentifier identifying a subject entity, and (b) one or more ontologyconcept identifiers corresponding respectively to one or more conceptsdefined within a graph-based domain ontology.

And in another embodiment, these also include automatically generating acontainer tree data structure comprising a data artifact packetcontainer node as a root node based at least in part on the dataartifact packet data object, wherein (a) the container tree datastructure comprises a plurality of container nodes that are descendantsof the root node based at least in part on the data artifact packet dataobject, (b) each container node of the container tree data structurecorresponds to one pair of a plurality of observable-value pairs in thedata artifact packet data object; and automatically traversing thecontainer tree data structure in a depth-first traversal to generate adata transfer object for each of container node of the plurality ofcontainer nodes, wherein each data transfer object corresponds to onepair of the plurality of observable-value pairs.

In accordance with one aspect, methods, systems, apparatus, computerprogram products, and/or the like are provided. In one embodiment, thesemay include automatically identifying an XML rule document comprising arule to be applied to data stored in one or more database objects,wherein the XML rule document (a) is identified based at least in parton a rule identifier, (b) defines one or more selection criteria for therule, (c) defines one or more condition criteria for the rule, (d)identifies an action to be performed, and (e) identifies an observablepacket data object based at least in part on an observable packet dataobject identifier; responsive to receiving, from a data store managementlayer in communication with a primary program, the data stored in theone or more database objects associated with an electronic record,programmatically evaluating the data using the one or more selectioncriteria defined by the rule; responsive to the one or more selectioncriteria of the rule being satisfied, evaluating the data using the oneor more condition criteria defined by the rule; and responsive to theone or more condition criteria of the rule being satisfied by the data:identifying the observable packet data object based at least in part onthe observable packet data object identifier, automatically populatingone or more fields in the observable packet data object with dataassociated with the electronic record, providing the populatedobservable packet data object to an ingestion processing module, andgenerating a data artifact packet based at least in part on thepopulated observable packet data object.

In one embodiment, these may also include automatically generating acontainer tree data structure comprising a data artifact packetcontainer node as a root node based at least in part on the dataartifact packet data object, wherein the container tree data structurecomprises a plurality of container nodes that are descendants of theroot node based at least in part on the data artifact packet dataobject; automatically traversing the container tree data structure in adepth-first traversal to generate a data transfer object for eachcontainer node; and automatically providing at least one of theplurality of data transfer objects for use in performing a databaseupdate function.

In a particular embodiment, these further include, wherein the rulesengine optimizes execution by (a) collapsing logical operators, and (b)reordering logical operators; wherein a rules engine schedulerautomatically executes the XML rule document at specific frequencies orin response to certain other criteria; wherein the certain othercriteria comprises a write function or an update function beingperformed to at least one of the one or more database objects by thedata store management layer; wherein at least one of the one or morefields is populated with an entity identifier associated with theelectronic record; and wherein each identifier is a universally uniqueidentifier or a globally unique identifier.

In still another embodiment, these may also include, wherein the XML,rule document further (a) identifies a second action to be performedwhen the condition criteria is not satisfied and (b) identifies a secondobservable packet data object based at least in part on a second XMLobservable packet data object identifier; and responsive to the one ormore condition criteria of the rule not being satisfied by the data:identifying the second observable packet data object based at least inpart on the second observable packet data object identifier,automatically populating one or more fields in the second observablepacket data object with data associated with the electronic record,providing the second populated observable packet data object to theingestion processing module, and generating a second data artifactpacket based at least in part on the second populated observable packetdata object.

In accordance with one aspect, methods, systems, apparatus, computerprogram products, and/or the like are provided. In one embodiment, thesemay include automatically receiving a message via one or morecommunications interfaces, wherein the message (a) comprises datacorresponding to the subject entity, and (b) is received from a sourcesystem; identifying a first attribute and a second attribute for thesubject entity from the message; generating an application programminginterface (API) request to an identity matching service, wherein (a) theAPI request comprises the first attribute and the second attribute, and(b) responsive to the API request, the identity matching service:accesses a rule set from a plurality of rule sets based at least in parton the source system, generates a first query based at least in part onthe rule set and the first attribute, queries a database, using thefirst query, for an electronic record stored in the databasecorresponding to the first attribute, responsive to an electronic recordnot being identified based at least in part on the first attribute,generates a second query based at least in part on the rule set and thesecond attribute, queries the database, using the second query, for anelectronic record stored in the database corresponding to the secondattribute, wherein the electronic record comprises an entity identifiercorresponding to the subject entity, and responsive to an electronicrecord being identified based at least in part on the second attribute,generates an API response comprising the entity identifier correspondingto the subject entity; receiving the API response comprising the entityidentifier corresponding to the subject entity; and automaticallygenerating a data artifact packet data object, wherein (a) the dataartifact packet data object comprises the entity identifier, (b) thedata artifact packet data object is generated based at least in part onan observable packet data object, and (b) the observable packet dataobject is an XML, document generated based at least in part on parsing amessage received from a source system.

In another embodiment, these may also include automatically storing thedata artifact packet data object in a database, wherein the dataartifact packet data object is identifiable by the corresponding entityidentifier; automatically detecting a user interaction eventcorresponding to a first entity identifier; responsive to detecting theuser interaction event corresponding to the first entity identifier,automatically retrieving the data artifact packet data objectcorresponding to the first entity identifier; providing the dataartifact packet data object corresponding to the first entity identifierto an ingestion processing module for processing; automaticallygenerating a container tree data structure comprising a data artifactpacket container node as a root node based at least in part on the dataartifact packet data object, wherein (a) the container tree datastructure comprises a plurality of container nodes that are descendantsof the root node based at least in part on the data artifact packet dataobject, (b) each container node of the container tree data structurecorresponds to one pair of a plurality of observable-value pairs in thedata artifact packet data object; automatically traversing the containertree data structure in a depth-first traversal to generate a datatransfer object for each of container node of the plurality of containernodes, wherein each data transfer object corresponds to one pair of theplurality of observable-value pairs; and automatically providing atleast one of the plurality of data transfer objects for use inperforming a database update function, wherein at least one containernode comprises at least one of a source vocabulary description, sourcevocabulary code, or text description of an observable, and (c) duringthe depth-first traversal of the container tree data structure, anaggregator method resolves the at least one of the source vocabularydescription, the source vocabulary code, or the text description of theobservable.

In still another embodiment, these may still include, whereinautomatically generating the container tree data structure comprises:determining a type of container node for each of the plurality ofobservable-value pairs based at least in part on the graph-based domainontology; determining whether a container node having the determinedtype is present in the container tree data structure; and responsive todetermining that a container node having the determined type is presentin the container tree data structure, storing the observable-value pairin the container node.

In yet another embodiment, these may also include, wherein automaticallygenerating the container tree data structure comprises: determining atype of container node for each of the plurality of observable-valuepairs based at least in part on the graph-based domain ontology;determining whether a container node having the determined type ispresent in the container tree data structure; and responsive todetermining that a container node having the determined type is notpresent in the container tree data structure: (a) constructing thecontainer node having the determine type, (b) inserting the containernode into an appropriate position in the container tree data structure,wherein the appropriate position in the container tree data structure isdetermined based at least in part on the graph-based domain ontology,and (c) storing the observable-value pair in the container node, andwherein the electronic record is identified based at least in part onthe second attribute when the electronic record comprises a recordattribute that (a) corresponds to the second attribute and (b) has asame value as the second attribute.

In accordance with one aspect, methods, systems, apparatus, computerprogram products, and/or the like are provided. In one embodiment, thesemay include automatically identifying a first container node of acontainer tree data structure by traversing the container tree datastructure in a depth-first traversal, wherein (a) the container treedata structure comprises a data artifact packet container node as a rootnode, (b) the container tree data structure is generated based at leastin part on a data artifact packet data object, (c) the container treedata structure comprises a plurality of container nodes that aredescendants of the root node based at least in part on the data artifactpacket data object, (d) the plurality of container nodes comprises thefirst container node, and (e) the first container node comprises anobservable; generating a data transfer object for the first containernode; invoking programmatic reasoning logic to determine an ontologyconcept identifier for the observable of the first container node basedat least in part on a graph-based domain ontology; querying, using theprogrammatic reasoning logic, a data store to identify the ontologyconcept identifier for the observable of the first container node; andreceiving, using the programmatic reasoning logic, a response to thequery, wherein the response to the query comprises one of (a) theontology concept identifier, or (b) a false response; and in an instancein which the response to the query comprises the ontology conceptidentifier, providing, using the programmatic reasoning logic, theontology concept identifier for storage in the data transfer object.

In one embodiment, these may also include, in an instance in which theresponse to the query comprises the false response, generating adescription for the observable; determining a relationship between thedescription and a first class of the graph-based domain ontology based;and inserting a new node in the graph-based domain ontology based forthe observable based at least in part on the description.

In yet another embodiment, these may further include, wherein the queryis submitted to one of (a) a cache data store, or (b) the graph-baseddomain ontology, wherein the programmatic reasoning logic is invokedfrom another process, and wherein each of the plurality of containernodes comprises an observable-value pair.

In another embodiment, these may further include, wherein generating thecontainer tree data structure comprises: determining a type of containernode for an observable-value pair of a plurality of observable-valuepairs based at least in part on the graph-based domain ontology;determining whether a container node having the determined type ispresent in the container tree data structure; and responsive todetermining that a container node having the determined type is presentin the container tree data structure, storing the observable-value pairin the container node.

And in still another embodiment, these may include, wherein generatingthe container tree data structure comprises: determining a type ofcontainer node for an observable-value pair of a plurality ofobservable-value pairs based at least in part on the graph-based domainontology; determining whether a container node having the determinedtype is present in the container tree data structure; and responsive todetermining that a container node having the determined type is notpresent in the container tree data structure: (a) constructing thecontainer node having the determine type, (b) inserting the containernode into an appropriate position in the container tree data structure,wherein the appropriate position in the container tree data structure isdetermined based at least in part on the graph-based domain ontology,and (c) storing the observable-value pair in the container node.

In accordance with one aspect, methods, systems, apparatus, computerprogram products, and/or the like are provided. In one embodiment, thesemay include receiving a request indicating at least a function to beperformed on an electronic record or an access requested to theelectronic record, wherein (a) the request originates from a requestingentity identifiable by a requesting entity identifier, (b) theelectronic record is associated with a subject entity identifiable by asubject entity identifier; responsive to receiving the request:determining a relationship type between the requesting entity and thesubject entity, wherein the relationship type is a direct relationshipor an indirect relationship, and determining a relationship statusbetween the requesting entity and the subject entity, wherein therelationship type is (a) an active relationship, or (b) an inactiverelationship; response to determining that (a) the relationship type isa direct relationship, and the (b) the relationship status is an activerelationship: identifying a user role for the requesting entity withrespect to the electronic record of the subject entity, and identifyinga rights group associated with the user role, wherein (a) the rightsgroup comprises one or more rights stored in a rights group data object,(b) the one or more rights allow the function to be performed on theelectronic record or the access requested to the electronic record, and(c) the rights data object comprises a corresponding key; and enablingthe function to be performed on the electronic record or the accessrequested to the electronic record based at least in part on thecorresponding key. These may also include providing the correspondingkey to a code module that enables the function to be performed on theelectronic record or the access requested to the electronic record basedat least in part on the corresponding key.

In one embodiment, these may include, wherein the requesting entity isrepresented as a node defined within a graph-based domain ontology thatis identifiable by the requesting entity identifier, and (d) the subjectentity is represented as a node defined within the graph-based domainontology that is identifiable by the subject entity identifier; whereinthe relationship type is (a) a direct relationship in an instance inwhich the node representing the requesting entity is connected to thenode representing the subject entity by one edge, and (b) an indirectrelationship in an instance in which the node representing therequesting entity is connected to the node representing the subjectentity by at least one intermediate node; and wherein the relationshiptype is determined by a preferred path between the node representing therequesting entity and the node representing the subject entity, whereinthe preferred path comprises the least number of intermediate nodes.

In another embodiment, these may include, wherein (a) the requestingentity is associated with a requesting entity relationship data objectthat is stored in a relationship table and that represents arelationship to the subject entity, (b) the requesting entityrelationship data object is identifiable based at least in part on therequesting entity identifier, (c) the subject entity is associated witha subject entity relationship data object that is stored in therelationship table and that represents a relationship to the requestingentity, and (d) the subject entity relationship data object isidentifiable based at least in part on the subject entity identifier.Additionally, these may include querying a database index for therelationship table for entity relationship data object and the subjectentity relationship data object, wherein the relationship type and therelationship status are determined based at least in part on therequesting entity relationship data object and the subject entityrelationship data object.

In still another embodiment, these may include, wherein the relationshipstatus is associated with a future end date, and wherein (a) the userrole corresponds to a class of data in the electronic record, and (b)the class of data is defined by a graph-based domain ontology.

In in yet another embodiment, these may include (a) generating anextractable packet data object, and (b) generating a data artifactpacket data object based at least in part on the extractable packet dataobject, wherein the data artifact data object comprises (a) the subjectentity identifier identifying a subject entity, and (b) one or moreontology concept identifiers corresponding respectively to one or moreconcepts defined within a graph-based domain ontology.

And in another embodiment, an extraction processing module:automatically generates a container tree data structure comprising adata artifact container node as the root node based at least in part onthe data artifact packet data object, wherein (a) the container treedata structure comprises a plurality of container nodes that aredescendants of the root node based at least in part on the data artifactpacket data object, (b) each container node of plurality of containernodes comprises an observable and an empty value for the correspondingobservable, (c) each empty value is to be retrieved from a database oraggregated from retrieved empty values; automatically traverses each ofthe plurality of container nodes of the container tree data structure ina depth-first traversal, wherein (a) at each container node that is aleaf node in the traversal, a method is executed to retrieve a non-emptyvalue from the database for the corresponding observable, and (b) at thecompletion of the traversal, each of the plurality of container nodescomprises a non-empty value for the corresponding observable; after thedepth-first traversal, automatically processes the container tree datastructure to generate at least one observable group, wherein the atleast one observable group comprises each observable and thecorresponding non-empty value; and generates, based at least in part onthe observable groups, an information message comprising the observablegroup for the function.

In yet another embodiment, automatically generating the container treedata structure comprises: determining a type of container node thatshould contain an observable corresponding to an ontology conceptidentifier in the data artifact packet data object; determining whethera container node having the determined type is present in the containertree data structure; and responsive to determining that a container nodehaving the determined type is present in the container tree datastructure, storing the observable and a corresponding empty value in thecontainer node, wherein (a) the depth-first traversal of the containertree data structure comprises aggregating two or more values of asubcontainer node to generate a value of container comprising thesubcontainer, and (b) wherein the extractable packet data object is anXML document.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

Having thus described embodiments of the present invention in generalterms, reference will now be made to the accompanying drawings, whichare not necessarily drawn to scale, and wherein:

FIG. 1 is a diagram of a system that can be used in conjunction withvarious embodiments of the present invention;

FIG. 2A is a schematic of a server in accordance with certainembodiments of the present invention;

FIG. 2B is a schematic representation of a memory media storing aplurality of data assets;

FIG. 3 is a schematic of a user computing entity in accordance withcertain embodiments of the present invention;

FIG. 4 illustrates an example ontology definition in accordance withcertain embodiments of the present invention;

FIGS. 5A, 5B, and 5C each illustrate a slice of a graph data structurerepresenting a portion of a graph-based domain ontology in accordancewith certain embodiments of the present invention;

FIG. 6 illustrates a portion of an example ontology definition of theclass “obstructive lung disease” and corresponding relationships toother classes in accordance with certain embodiments of the presentinvention;

FIG. 7A is a data flow diagram illustrating an information/data flow forvarious processes for managing, ingesting, monitoring, updating, and/orextracting/retrieving one or more ERs stored in the ER data store inaccordance with certain embodiments of the present invention;

FIG. 7B is a data flow diagram illustrating an information/data flow forcarious processes for retrieving information/data from one or more ERsstored in the ER data store in accordance with certain embodiments ofthe present invention;

FIG. 8 is a flowchart illustrating various processes, procedures, and/oroperations performed, for instance, by a server of FIGS. 2A and 2B, toperform pre-processing of a message, in accordance with certainembodiments of the present invention;

FIG. 9 illustrates an example message in accordance with certainembodiments of the present invention;

FIG. 10A illustrates an example observable packet data object (OPDO) inaccordance with certain embodiments of the present invention;

FIG. 10B illustrates an example data artifact packet tree data structurein accordance with certain embodiments of the present invention;

FIG. 10C illustrates an example extractable packet data object (EPDO) inaccordance with certain embodiments of the present invention;

FIGS. 11A and 11B are schematic diagrams illustrating the extraction ofattributes corresponding to entities from message data, in accordancewith certain embodiments of the present invention;

FIG. 12 is a flowchart illustrating various processes, procedures,and/or operations performed, for example, by a server of FIGS. 2A and2B, to perform entity matching, in accordance with certain embodimentsof the present invention;

FIG. 13 is a flowchart illustrating various processes, procedures,and/or operations performed, for instance, by a server of FIGS. 2A and2B, to perform processing of a message data artifact packet data objectto update one or more ERs stored in an ER data store, in accordance withcertain embodiments of the present invention;

FIG. 14 is a flowchart illustrating various processes, procedures,and/or operations performed, for example, by a server of FIGS. 2A and2B, to construct a container tree data structure, in accordance withcertain embodiments of the present invention;

FIG. 15 is a flowchart illustrating various processes, procedures,and/or operations performed, for instance, by a server of FIGS. 2A and2B, to perform deferred processing (e.g., user interaction eventprocessing) and/or prioritized processing, in accordance with certainembodiments of the present invention;

FIG. 16 is a flowchart illustrating various processes, procedures,and/or operations performed, for example, by a server of FIGS. 2A and2B, to update an SBRO of an ER, in accordance with certain embodimentsof the present invention;

FIGS. 17A and 17B are flowcharts illustrating various processes,procedures, and/or operations performed, for instance, by a server ofFIGS. 2A and 2B, to reason against a graph-based domain ontology, inaccordance with certain embodiments of the present invention;

FIG. 18 is a flowchart illustrating various processes, procedures,and/or operations performed, for example, by a server of FIGS. 2A and2B, to determine a confidence score for an SBRO, in accordance withcertain embodiments of the present invention;

FIG. 19 is a flowchart illustrating various processes, procedures,and/or operations performed, for instance, by a server of FIGS. 2A and2B, to interact with the persistent storage of the ER data store, inaccordance with certain embodiments;

FIG. 20 is a flowchart illustrating various processes, procedures,and/or operations performed, for example, by a server of FIGS. 2A and2B, to complete an access request corresponding to the ER data store invarious scenarios, in accordance with certain embodiments;

FIG. 21 is a flowchart illustrating various processes, procedures,and/or operations performed, for instance, by a server of FIGS. 2A and2B, to complete an access request corresponding to the ER data store inother scenarios, in accordance with certain embodiments;

FIG. 22 is a flowchart illustrating various processes, procedures,and/or operations performed, for example, by a server of FIGS. 2A and2B, to complete a write request corresponding to the ER data store, inaccordance with certain embodiments of the present invention;

FIG. 23 is a flowchart illustrating various processes, procedures,and/or operations performed, for instance, by a server of FIGS. 2A and2B, to synchronize an ER stored in the ER data store with a persistentobject, in accordance with certain embodiments of the present invention;

FIG. 24 is a flowchart illustrating various processes, procedures,and/or operations performed, for example, by a server of FIGS. 2A and2B, to update an ER record based at least in part on evaluation of arule based at least in part on the ER record, in accordance with certainembodiments of the present invention;

FIGS. 25A-25T illustrates example formats for defining various portionsof a rule, in accordance with certain embodiments of the presentinvention;

FIG. 26 is a block diagram illustrating various components related to auser accessing information/data of an ER stored in the ER data store, inaccordance with certain embodiments of the present invention;

FIGS. 27 and 28 are schematic diagrams illustrating how access toinformation/data stored in the ER data store is controlled in accordancewith certain embodiments;

FIG. 29 is a flowchart illustrating various processes, procedures,and/or operations performed, for example, by a server of FIGS. 2A and2B, to control access to information/data of an ER stored in the ER datastore, in accordance with certain embodiments of the present invention;

FIGS. 30A and 30B each illustrate an example portion of a graph datastructure (e.g., relationship graph) defined by a graph-based domainontology, in accordance with certain embodiments;

FIG. 31 is a flowchart illustrating various processes, procedures,and/or operations performed, for instance, by a server of FIGS. 2A and2B, to process an extractable packet data object to generate/create oneor more observation groups comprising information/data from an ER storedin the ER data store, in accordance with certain embodiments of thepresent invention;

FIGS. 32, 33, 34A, 34B, 35, and 36 illustrate exemplary screenshots ofinterfaces and outputs, in accordance with certain embodiments; and

FIG. 37 illustrates exemplary relationships for data access and functioncontrol, in accordance with certain embodiments.

DETAILED DESCRIPTION OF SOME EXAMPLE EMBODIMENTS

Various embodiments of the present invention now will be described morefully hereinafter with reference to the accompanying drawings, in whichsome, but not all embodiments of the inventions are shown. Indeed, theseinventions may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. The term “or” (also designated as “/”) is usedherein in both the alternative and conjunctive sense, unless otherwiseindicated. The terms “illustrative” and “exemplary” are used to beexamples with no indication of quality level. Like numbers refer to likeelements throughout.

I. COMPUTER PROGRAM PRODUCTS, METHODS, AND COMPUTING ENTITIES

Embodiments of the present invention may be implemented in various ways,including as computer program products that comprise articles ofmanufacture. Such computer program products may include one or moresoftware components including, for example, software objects, methods,data structures, and/or the like. A software component may be coded inany of a variety of programming languages. An illustrative programminglanguage may be a lower-level programming language such as an assemblylanguage associated with a particular hardware architecture and/oroperating system platform. A software component comprising assemblylanguage instructions may require conversion into executable machinecode by an assembler prior to execution by the hardware architectureand/or platform. Another example programming language may be ahigher-level programming language that may be portable across multiplearchitectures. A software component comprising higher-level programminglanguage instructions may require a conversion to an intermediaterepresentation by an interpreter or a compiler prior to execution.

Other examples of programming languages include, but are not limited to,a macro language, a shell or command language, a job control language, ascript language, a database query or search language, and/or a reportwriting language. In one or more example embodiments, a softwarecomponent comprising instructions in one of the foregoing examples ofprogramming languages may be executed directly by an operating system orother software component without having to be first transformed intoanother form. A software component may be stored as a file or other datastore construct. Software components of a similar type or functionallyrelated may be stored together such as, for instance, in a particulardirectory, folder, or library. Software components may be static (e.g.,pre-established or fixed) or dynamic (e.g., created or modified at thetime of execution).

A computer program product may include a non-transitorycomputer-readable storage medium storing applications, programs, programmodules, scripts, source code, program code, object code, byte code,compiled code, interpreted code, machine code, executable instructions,and/or the like (also referred to herein as executable instructions,instructions for execution, computer program products, program code,and/or similar terms used herein interchangeably). Such non-transitorycomputer-readable storage media include all computer-readable media(including volatile and non-volatile media).

In one embodiment, a non-volatile computer-readable storage medium mayinclude a floppy disk, flexible disk, hard disk, solid-state storage(SSS) (e.g., a solid state drive (SSD), solid state card (SSC), solidstate module (SSM), enterprise flash drive, magnetic tape, or any othernon-transitory magnetic medium, and/or the like. A non-volatilecomputer-readable storage medium may also include a punch card, papertape, optical mark sheet (or any other physical medium with patterns ofholes or other optically recognizable indicia), compact disc read onlymemory (CD-ROM), compact disc-rewritable (CD-RW), digital versatile disc(DVD), Blu-ray disc (BD), any other non-transitory optical medium,and/or the like. Such a non-volatile computer-readable storage mediummay also include read-only memory (ROM), programmable read-only memory(PROM), erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), flash memory (e.g.,Serial, NAND, NOR, and/or the like), multimedia memory cards (MMC),secure digital (SD) memory cards, SmartMedia cards, CompactFlash (CF)cards, Memory Sticks, and/or the like. Further, a non-volatilecomputer-readable storage medium may also include conductive-bridgingrandom access memory (CBRAM), phase-change random access memory (PRAM),ferroelectric random-access memory (FeRAIVI), non-volatile random-accessmemory (NVRAM), magnetoresistive random-access memory (MRAM), resistiverandom-access memory (RRAM), Silicon-Oxide-Nitride-Oxide-Silicon memory(SONOS), floating junction gate random access memory (FJG RAM),Millipede memory, racetrack memory, and/or the like.

In one embodiment, a volatile computer-readable storage medium mayinclude random access memory (RAM), dynamic random access memory (DRAM),static random access memory (SRAM), fast page mode dynamic random accessmemory (FPM DRAM), extended data-out dynamic random access memory (EDODRAM), synchronous dynamic random access memory (SDRAM), double datarate synchronous dynamic random access memory (DDR SDRAM), double datarate type two synchronous dynamic random access memory (DDR2 SDRAM),double data rate type three synchronous dynamic random access memory(DDR3 SDRAM), Rambus dynamic random access memory (RDRAM), TwinTransistor RAM (TTRAM), Thyristor RAM (T-RAM), Zero-capacitor (Z-RAM),Rambus in-line memory module (RIMM), dual in-line memory module (DIMM),single in-line memory module (SIMM), video random access memory (VRAM),cache memory (including various levels), flash memory, register memory,and/or the like. It will be appreciated that where embodiments aredescribed to use a computer-readable storage medium, other types ofcomputer-readable storage media may be substituted for or used inaddition to the computer-readable storage media described above.

As should be appreciated, various embodiments of the present inventionmay also be implemented as methods, apparatus, systems, computingdevices, computing entities, and/or the like. As such, embodiments ofthe present invention may take the form of a data structure, apparatus,system, computing device, computing entity, and/or the like executinginstructions stored on a computer-readable storage medium to performcertain steps or operations. Thus, embodiments of the present inventionmay also take the form of an entirely hardware embodiment, an entirelycomputer program product embodiment, and/or an embodiment that comprisesa combination of computer program products and hardware performingcertain steps or operations.

Embodiments of the present invention are described below with referenceto block diagrams and flowchart illustrations. Thus, it should beunderstood that each block of the block diagrams and flowchartillustrations may be implemented in the form of a computer programproduct, an entirely hardware embodiment, a combination of hardware andcomputer program products, and/or apparatus, systems, computing devices,computing entities, and/or the like executing instructions, operations,steps, and similar words used interchangeably (e.g., the executableinstructions, instructions for execution, program code, and/or the like)on a computer-readable storage medium for execution. For instance,retrieval, loading, and execution of code may be performed sequentiallysuch that one instruction is extracted/retrieved, loaded, and executedat a time. In some exemplary embodiments, retrieval, loading, and/orexecution may be performed in parallel such that multiple instructionsare extracted/retrieved, loaded, and/or executed together. Thus, suchembodiments can produce specifically-configured machines, performing thesteps or operations specified in the block diagrams and flowchartillustrations. Accordingly, the block diagrams and flowchartillustrations support various combinations of embodiments for performingthe specified instructions, operations, or steps.

II. EXEMPLARY PLATFORM/SYSTEM ARCHITECTURE

FIG. 1 provides an illustration of a platform/system architecture 100(also referred herein as the ER system 100) that can be used inconjunction with various embodiments of the present invention. As shownin FIG. 1, the ER system 100 may comprise one or more servers 65, one ormore source systems 40, one or more user computing entities 30, one ormore networks 135, and/or the like. In various embodiments, the one ormore user computing entities 30 may comprise provider computingentities, patient/member computing entities, and/or computing entitiesoperated by and/or on behalf of other entities. In various embodiments,the source systems 40 comprise claims processing systems, laboratorycomputing entities, user computing entities 30, and/or the like. Each ofthe components of the ER system 100 may be in electronic communicationwith, for example, one another over the same or different wireless orwired networks 135 including, for instance, a wired or wireless PersonalArea Network (PAN), Local Area Network (LAN), Metropolitan Area Network(MAN), Wide Area Network (WAN), and/or the like. Additionally, whileFIG. 1 illustrates certain system entities as separate, standaloneentities, the various embodiments are not limited to this particulararchitecture.

Exemplary Server

FIG. 2A provides a schematic of a server 65 according to one embodimentof the present invention. In various embodiments, the server 65 (e.g.,one or more servers, server systems, computing entities, and/or similarwords used herein interchangeably) executes one or more program modules,application program code, sets of computer executable instructions,and/or the like to receive and process messages based at least in parton a graph-based domain ontology, generate/create, update, and/or managean electronic record (ER) database, provide and/or control user accessto information/data stored in the ER data store 211 (e.g., database),and/or the like. In general, the terms server, computing entity, entity,device, system, and/or similar words used herein interchangeably mayrefer to, for example, one or more computers, computing entities,desktop computers, mobile phones, tablets, phablets, notebooks, laptops,distributed systems, items/devices, terminals, servers or servernetworks, blades, gateways, switches, processing devices, processingentities, set-top boxes, relays, routers, network access points, basestations, the like, and/or any combination of devices or entitiesadapted to perform the functions, operations, and/or processes describedherein. Such functions, operations, and/or processes may include, forinstance, transmitting, receiving, operating on, processing, displaying,storing, determining, generating/creating, monitoring, evaluating,comparing, and/or similar terms used herein interchangeably. In oneembodiment, these functions, operations, and/or processes can beperformed on data, content, information, and/or similar terms usedherein interchangeably.

As indicated, in one embodiment, the server 65 may also include one ormore network and/or communications interfaces 208 for communicating withvarious computing entities, such as by communicating data, content,information, and/or similar terms used herein interchangeably that canbe transmitted, received, operated on, processed, displayed, stored,and/or the like. For instance, the server 65 may communicate with othercomputing entities, one or more user computing entities 30, one or moresource systems 40, and/or the like.

As shown in FIG. 2A, in one embodiment, the server 65 may include or bein communication with one or more processing elements 205 (also referredto as processors, processing circuitry, and/or similar terms used hereininterchangeably) that communicate with other elements within the server65 via a bus, for example, or network connection. As will be understood,the processing element 205 may be embodied in a number of differentways. For example, the processing element 205 may be embodied as one ormore complex programmable logic devices (CPLDs), microprocessors,multi-core processors, coprocessing entities, application-specificinstruction-set processors (ASIPs), and/or controllers. Further, theprocessing element 205 may be embodied as one or more other processingdevices or circuitry. As noted in the definitions, the term circuitrymay refer to an entirely hardware embodiment or a combination ofhardware and computer program products. Thus, the processing element 205may be embodied as integrated circuits, application specific integratedcircuits (ASICs), field programmable gate arrays (FPGAs), programmablelogic arrays (PLAs), hardware accelerators, other circuitry, and/or thelike. As will therefore be understood, the processing element 205 may beconfigured for a particular use or configured to execute instructionsstored in volatile or non-volatile media or otherwise accessible to theprocessing element 205. As such, whether configured by hardware orcomputer program products, or by a combination thereof, the processingelement 205 may be capable of performing steps or operations accordingto embodiments of the present invention when configured accordingly.

In one embodiment, the server 65 may further include or be incommunication with non-volatile media (also referred to as non-volatilestorage, memory, memory storage, memory circuitry and/or similar termsused herein interchangeably). In one embodiment, the non-volatilestorage or memory may include one or more non-volatile storage or memorymedia 206 as described above, such as hard disks, ROM, PROM, EPROM,EEPROM, flash memory, MMCs, SD memory cards, Memory Sticks, CBRAM, PRAM,FeRAM, RRAM, SONOS, racetrack memory, and/or the like. As will berecognized, the non-volatile storage or memory media may storedatabases, metadata repositories database instances, database managementsystem entities, data, applications, programs, program modules, scripts,source code, object code, byte code, compiled code, interpreted code,machine code, executable instructions, and/or the like. For instance, asshown in FIG. 2B, the memory media 206 may store computer executablecode that, when executed by the processing element 205, causes theoperation of a message pre-processing module 220, ingestion processingmodule 225, programmatic reasoning logic 230, confidence score engine235, identity matching service 240, data store management layer (DSML)245, the rules engine 250, data access/function controller 255,extraction processing module 260, SBRO processing module 265, and/or thelike, which are described in detail elsewhere herein. Though describedas modules herein, the message pre-processing module 220, ingestionprocessing module 225, programmatic reasoning logic 230, confidencescore engine 235, identity matching service 240, the DSML 245, rulesengine 250, data access/function controller 255, extraction processingmodule 260, and/or SBRO processing module 265 may be embodied by variousforms of computer-executable instructions, program/application code,and/or the like, in various embodiments. The terms “database,” “databaseinstance,” “database management system,” and/or similar terms usedherein interchangeably and in a general sense to refer to a structuredor unstructured collection of information/data that is stored in acomputer-readable storage medium.

Memory media 206 (e.g., metadata repository) may also be embodied as adata store device or devices, as a separate database server or servers,or as a combination of data store devices and separate database servers.Further, in some embodiments, memory media 206 may be embodied as adistributed repository such that some of the stored information/data isstored centrally in a location within the ER system 100 and otherinformation/data is stored in one or more remote locations.Alternatively, in some embodiments, the distributed repository may bedistributed over a plurality of remote storage locations only. Anexample of the embodiments contemplated herein would include a clouddata store system maintained by a third party provider and where some orall of the information/data required for the operation of the ER system100 may be stored. As a person of ordinary skill in the art wouldrecognize, the information/data required for the operation of the ERsystem 100 may also be partially stored in the cloud data store systemand partially stored in a locally maintained data store system.

Memory media 206 (e.g., metadata repository) may includeinformation/data accessed and stored by the ER system 100 to facilitatethe operations of the ER system 100. More specifically, memory media 206may encompass one or more data stores configured to storeinformation/data usable in certain embodiments. For example, as shown inFIG. 2B, metadata for data assets may be stored in metadata repositoriesencompassed within the memory media 206. The metadata for the dataassets in the metadata data stores, metadata repositories, and similarwords used herein interchangeably may comprise ER data store 211,ontology data 212, source system data 213, DBO class data 214, and/orvarious other types of information/data. In an example embodiment, thememory media 206 may store patient/member data repositories (ERs),provider data repositories, care standard data repositories, and/or thelike. As will be recognized, metadata repositories are inventories dataassets in an organization's environment.

In one embodiment, the server 65 may further include or be incommunication with volatile media (also referred to as volatile storage,memory, memory storage, memory circuitry and/or similar terms usedherein interchangeably). In one embodiment, the volatile storage ormemory may also include one or more volatile storage or memory media 207as described above, such as RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM,DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, RIMM, DIMM, SIMM, VRAM, cachememory, register memory, and/or the like. As will be recognized, thevolatile storage or memory media may be used to store at least portionsof the databases, database instances, database management systementities, data, applications, programs, program modules, scripts, sourcecode, object code, byte code, compiled code, interpreted code, machinecode, executable instructions, and/or the like being executed by, forinstance, the processing element 205. Thus, the databases, databaseinstances, database management system entities, data, applications,programs, program modules, scripts, source code, object code, byte code,compiled code, interpreted code, machine code, executable instructions,and/or the like may be used to control certain aspects of the operationof the server 65 with the assistance of the processing element 205 andoperating system.

As indicated, in one embodiment, the server 65 may also include one ormore network and/or communications interfaces 208 for communicating withvarious computing entities, such as by communicating data, content,information, and/or similar terms used herein interchangeably that canbe transmitted, received, operated on, processed, displayed, stored,and/or the like. For instance, the server 65 may communicate withcomputing entities or communications interfaces of other servers 65,user computing entities 30, and/or the like. In this regard, the server65 may access various data assets.

As indicated, in one embodiment, the server 65 may also include one ormore network and/or communications interfaces 208 for communicating withvarious computing entities, such as by communicating data, content,information, and/or similar terms used herein interchangeably that canbe transmitted, received, operated on, processed, displayed, stored,and/or the like. Such communication may be executed using a wired datatransmission protocol, such as fiber distributed data interface (FDDI),digital subscriber line (DSL), Ethernet, asynchronous transfer mode(ATM), frame relay, data over cable service interface specification(DOCSIS), or any other wired transmission protocol. Similarly, theserver 65 may be configured to communicate via wireless externalcommunication networks using any of a variety of protocols, such asgeneral packet radio service (GPRS), Universal Mobile TelecommunicationsSystem (UMTS), Code Division Multiple Access 2000 (CDMA2000), CDMA20001× (1×RTT), Wideband Code Division Multiple Access (WCDMA), GlobalSystem for Mobile Communications (GSM), Enhanced Data rates for GSMEvolution (EDGE), Time Division-Synchronous Code Division MultipleAccess (TD-SCDMA), Long Term Evolution (LTE), Evolved UniversalTerrestrial Radio Access Network (E-UTRAN), Evolution-Data Optimized(EVDO), High Speed Packet Access (HSPA), High-Speed Downlink PacketAccess (HSDPA), IEEE 802.11 (Wi-Fi), Wi-Fi Direct, 802.16 (WiMAX),ultra-wideband (UWB), infrared (IR) protocols, near field communication(NFC) protocols, Wibree, Bluetooth protocols, wireless universal serialbus (USB) protocols, and/or any other wireless protocol. The server 65may use such protocols and standards to communicate using Border GatewayProtocol (BGP), Dynamic Host Configuration Protocol (DHCP), Domain NameSystem (DNS), File Transfer Protocol (FTP), Hypertext Transfer Protocol(HTTP), HTTP over TLS/SSL/Secure, Internet Message Access Protocol(IMAP), Network Time Protocol (NTP), Simple Mail Transfer Protocol(SMTP), Telnet, Transport Layer Security (TLS), Secure Sockets Layer(SSL), Internet Protocol (IP), Transmission Control Protocol (TCP), UserDatagram Protocol (UDP), Datagram Congestion Control Protocol (DCCP),Stream Control Transmission Protocol (SCTP), HyperText Markup Language(HTML), and/or the like.

As will be appreciated, one or more of the server's components may belocated remotely from other server 65 components, such as in adistributed system. Furthermore, one or more of the components may beaggregated and additional components performing functions describedherein may be included in the server 65. Thus, the server 65 can beadapted to accommodate a variety of needs and circumstances.

Exemplary User Computing Entity

FIG. 3 provides an illustrative schematic representative of usercomputing entity 30 that can be used in conjunction with embodiments ofthe present invention. In various embodiments, a user computing entity30 may be a provider computing entity operated by and/or on behalf of aprovider. In various embodiments, a provider is a service provider. Forinstance, in an example embodiment, a provider is a healthcare provider;clinic; hospital; healthcare provider group; administrative and/orclinical staff associated with a healthcare provider, clinic, hospital,healthcare provider group, and/or the like; and/or other provider ofhealthcare services. In various embodiments, a user computing entity 30is a patient/member computing entity. In various embodiments, a usercomputing entity 30 operates and/or is in communication with (e.g., is aclient, thin client, and/or the like) a computing entity operating afront end and/or user-facing program. For example, a user computingentity 30 may provide a user with access to a portal, one or moreportlets, and/or the like for causing one or more messages to begenerated/created and provided such that the messages are received andprocessed by a server 65 and/or to access information/data stored in theER data store (e.g., database) 211.

As will be recognized, the user computing entity 30 may be operated byan agent and include components and features similar to those describedin conjunction with the server 65. Further, as shown in FIG. 3, the usercomputing entity may include additional components and features. Forinstance, the user computing entity 30 can include an antenna 312, atransmitter 304 (e.g., radio), a receiver 306 (e.g., radio), and aprocessing element 308 that provides signals to and receives signalsfrom the transmitter 304 and receiver 306, respectively. The signalsprovided to and received from the transmitter 304 and the receiver 306,respectively, may include signaling information/data in accordance withan air interface standard of applicable wireless systems to communicatewith various entities, such as a server 65, another user computingentity 30, and/or the like. In this regard, the user computing entity 30may be capable of operating with one or more air interface standards,communication protocols, modulation types, and access types. Moreparticularly, the user computing entity 30 may operate in accordancewith any of a number of wireless communication standards and protocols.In a particular embodiment, the user computing entity 30 may operate inaccordance with multiple wireless communication standards and protocols,such as GPRS, UMTS, CDMA2000, 1×RTT, WCDMA, TD-SCDMA, LTE, E-UTRAN,EVDO, HSPA, HSDPA, Wi-Fi, WiMAX, UWB, IR protocols, Bluetooth protocols,USB protocols, and/or any other wireless protocol.

Via these communication standards and protocols, the user computingentity 30 can communicate with various other entities using conceptssuch as Unstructured Supplementary Service data (USSD), Short MessageService (SMS), Multimedia Messaging Service (MMS), Dual-ToneMulti-Frequency Signaling (DTMF), and/or Subscriber Identity ModuleDialer (SIM dialer). The user computing entity 30 can also downloadchanges, add-ons, and updates, for instance, to its firmware, software(e.g., including executable instructions, applications, programmodules), and operating system.

According to one embodiment, the user computing entity 30 may includelocation determining aspects, devices, modules, functionalities, and/orsimilar words used herein interchangeably. For example, the usercomputing entity 30 may include outdoor positioning aspects, such as alocation module adapted to acquire, for example, latitude, longitude,altitude, geocode, course, direction, heading, speed, UTC, date, and/orvarious other information/data. In one embodiment, the location modulecan acquire data, sometimes known as ephemeris data, by identifying thenumber of satellites in view and the relative positions of thosesatellites. The satellites may be a variety of different satellites,including LEO satellite systems, DOD satellite systems, the EuropeanUnion Galileo positioning systems, the Chinese Compass navigationsystems, Indian Regional Navigational satellite systems, and/or thelike. Alternatively, the location information/data may be determined bytriangulating the position in connection with a variety of othersystems, including cellular towers, Wi-Fi access points, and/or thelike. Similarly, the user computing entity 30 may include indoorpositioning aspects, such as a location module adapted to acquire, forinstance, latitude, longitude, altitude, geocode, course, direction,heading, speed, time, date, and/or various other information/data. Someof the indoor aspects may use various position or location technologiesincluding RFID tags, indoor beacons or transmitters, Wi-Fi accesspoints, cellular towers, nearby computing devices (e.g., smartphones,laptops) and/or the like. For instance, such technologies may includeiBeacons, Gimbal proximity beacons, BLE transmitters, Near FieldCommunication (NFC) transmitters, and/or the like. These indoorpositioning aspects can be used in a variety of settings to determinethe location of someone or something to within inches or centimeters.

The user computing entity 30 may also comprise a user interfacecomprising one or more user input/output devices/interfaces (e.g., adisplay 316 and/or speaker/speaker driver coupled to a processingelement 308 and a touch screen, keyboard, mouse, and/or microphonecoupled to a processing element 308). For instance, the user outputdevice/interface may be configured to provide an application, browser,interactive user interface (IUI), dashboard, webpage, Internetaccessible/online portal, and/or similar words used hereininterchangeably executing on and/or accessible via the user computingentity 30 to cause display or audible presentation of information/dataand for user interaction therewith via one or more user inputdevices/interfaces. The user output interface may be updated dynamicallyfrom communication with the server 65. The user input device/interfacecan comprise any of a number of devices allowing the user computingentity 30 to receive information/data, such as a keypad 318 (hard orsoft), a touch display, voice/speech or motion interfaces, scanners,readers, or other input device. In embodiments including a keypad 318,the keypad 318 can include (or cause display of) the conventionalnumeric (0-9) and related keys (#, *), and other keys used for operatingthe user computing entity 30 and may include a full set of alphabetickeys or set of keys that may be activated to provide a full set ofalphanumeric keys. In addition to providing input, the user inputdevice/interface can be used, for example, to activate or deactivatecertain functions, such as screen savers and/or sleep modes. Throughsuch inputs, the user computing entity 30 can collect information/data,user interaction/input, and/or the like.

The user computing entity 30 can also include volatile storage or memory322 and/or non-volatile storage or memory 324, which can be embeddedand/or may be removable. For example, the non-volatile memory may beROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, MemorySticks, CBRAM, PRAM, FeRAM, RRAM, SONOS, racetrack memory, and/or thelike. The volatile memory may be RAM, DRAM, SRAM, FPM DRAM, EDO DRAM,SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, RIMM, DIMM, SIMM, VRAM,cache memory, register memory, and/or the like. The volatile andnon-volatile storage or memory can store databases, database instances,database management system entities, data, applications, programs,program modules, scripts, source code, object code, byte code, compiledcode, interpreted code, machine code, executable instructions, and/orthe like to implement the functions of the user computing entity 30.

Exemplary Source System

In various embodiments, source systems 40 are systems thatgenerate/create and provide messages such that the messages are receivedand processed by the server 65. For instance, source systems arecomputing entities that provide information/data (e.g., via one or moremessages) used to update one or more ER data stores 211 (e.g.,databases). In various embodiments, a source system 40 comprises one ormore components similar to components of a server 65 and/or usercomputing entity 30. For example, a source system 40 may comprise one ormore processing elements, volatile memory, non-volatile memory, one ormore communications interfaces, one or more network interfaces, one ormore antennae, one or more receivers, one or more transmitters, one ormore user interfaces, and/or the like.

Exemplary Networks

In one embodiment, the networks 135 may include, but are not limited to,any one or a combination of different types of suitable communicationsnetworks such as, for instance, cable networks, public networks (e.g.,the Internet), private networks (e.g., frame-relay networks), wirelessnetworks, cellular networks, telephone networks (e.g., a public switchedtelephone network), or any other suitable private and/or publicnetworks. Further, the networks 135 may have any suitable communicationrange associated therewith and may include, for example, global networks(e.g., the Internet), MANs, WANs, LANs, or PANs. In addition, thenetworks 135 may include any type of medium over which network trafficmay be carried including, but not limited to, coaxial cable,twisted-pair wire, optical fiber, a hybrid fiber coaxial (HFC) medium,microwave terrestrial transceivers, radio frequency communicationmediums, satellite communication mediums, or any combination thereof, aswell as a variety of network devices and computing platforms provided bynetwork providers or other entities.

III. EXEMPLARY DEFINITIONS

The term “or” is used herein in both the alternative and conjunctivesense, unless otherwise indicated. The terms “illustrative,” “example,”and “exemplary” are used to be examples with no indication of qualitylevel. Like numbers refer to like elements throughout.

The terms “comprising” means “including but not limited to” and shouldbe interpreted in the manner it is typically used in the patent context.Use of broader terms such as comprises, comprises, and having should beunderstood to provide support for narrower terms such as consisting of,consisting essentially of, and comprised substantially of.

The phrases “in one embodiment,” “according to one embodiment,” and thelike generally mean that the particular feature, structure, orcharacteristic following the phrase may be included in at least oneembodiment of the present disclosure, and may be included in more thanone embodiment of the present disclosure (importantly, such phrases donot necessarily refer to the same embodiment).

The terms “system,” “platform,” and/or the like refer to a softwareplatform and associated hardware that is configured to enable computingentities associated with user accounts of the ER system 100 to transmitcommunication resources associated with the user accounts and receivecommunication resources associated with the user accounts. Examples ofsystems include email systems, messaging systems, group-based systems,and/or the like. For example, an email system may communicate with afirst computing entity associated with an email user account to receivea first email communication resource intended to be sent by the emailuser account to a recipient entity, transmit the first emailcommunication to a second computing entity associated with the recipiententity (e.g., associated with a system of the recipient entity), receivea second email communication resource intended to be sent to the emailuser account from a third computing resource associated with a senderentity, and transmit the second email communication resource to thefirst computing entity associated with the email user account.

The terms “program layer,” “layer,” and/or the like may refer to anindependent operating component of a software program. In someembodiments, a layer may only have an interface to the layer above itand the layer below it.

The terms “module,” “programming module,” “engine,” and/or the like mayrefer to a software program that is independent, such that each contains(or has access to) what is necessary to execute the desiredfunctionality.

The terms “data store,” “data storage,” and/or the like may refer to arepository for persistently storing collections of data, such as adatabase, a file system or a directory.

The terms “electronic medical records,” “EMRs,” and/or the like mayrefer to records that contain medical information/data of the patients.

The terms “electronic record,” “ER,” and or the like may refer to adigital tool that provides an all-in-one record of a domain of interestfor an individual. For example, in the healthcare context, an ER may befor an individual's health, enabling a person and a care team to helpimprove collaboration and care (in this context the ER may also bereferred to as an electronic health record “EHR”). The ER comprisesvarious data objects (e.g., SBROs) and their correspondinginformation/data that can be stored, accessed, updated, and used topresent the data information/data contained there via a user interfaceor communicated to or within systems.

As used herein, the terms “data,” “content,” “digital content,” “digitalcontent object,” “information,” “object,” “data object,” “file,”“packet,” and similar terms may be used interchangeably to refer toinformation/data capable of being transmitted, received, and/or storedin accordance with embodiments of the present disclosure. Thus, use ofany such terms should not be taken to limit the spirit and scope ofembodiments of the present disclosure. Further, where a computing entityis described herein to receive information/data from another computingentity, it will be appreciated that the information/data may be receiveddirectly from another computing entity or may be received indirectly viaone or more intermediary computing entities, such as, for example, oneor more servers, relays, routers, network access points, base stations,hosts, and/or the like (sometimes referred to herein as a “network”).Similarly, where a computing entity is described herein to sendinformation/data to another computing entity, it will be appreciatedthat the information/data may be sent directly to another computingentity or may be sent indirectly via one or more intermediary computingentities, such as, for example, one or more servers, relays, routers,network access points, base stations, hosts, and/or the like.

The terms “data transfer object” and/or the like may refer to an objectthat carries information/data between processes, such as a class totransfer information/data between tiers in a multi-tiered architecture.In a particular embodiment, each data transfer object comprises akey-value pair (e.g., ontology concept identifier and itsassociated/corresponding value) and metadata associated with the same.The key-value pair of the data transfer object is compared to thekey-value pair of an SDBO, if one exists. Otherwise, the key-value pairof the data transfer object is used to generate/create an SDBO with thekey-value pair and its metadata.

The terms “ingestion,” “importation,” and/or the like may refer to theautomated processing and storage of information/data.

The terms “extraction,” “retrieval,” and/or the like may refer to theautomated retrieval of information/data.

The terms “single best record object,” “SBRO,” and/or the like may referto a data object storing the “single most accurate information/data,”“single most fit information/data,” “single most best information/data,”and/or the like for a particular ontology concept, such as anobservable. In a particular embodiment, each SBRO comprises a key-valuepair (e.g., ontology concept identifier and its associated/correspondingvalue) and/or corresponding metadata. The information/data is storedinto each SBRO by the ingestion processing module 225. Similarly, theinformation/data is extracted/retrieved from each SBRO by the extractionprocessing module 260. In the healthcare context, an SBRO may storeinformation associated with conditions, services, results, products,care relationships, and/or the like. For example, conditions may includemedical problems, medical history, family history, allergies, findings,and/or the like.

The terms “identifier” and/or the like may refer to an identifier thatuniquely identifies information/data stored that is related to anontology concept, an entity, a user, a user profile, a user account, auser group, an actor, and/or the like.

The terms “circuitry” should be understood broadly to include hardware,and in some embodiments, software for configuring the hardware. Withrespect to components of the apparatus, the term “circuitry” as usedherein should therefore be understood to include particular hardwareconfigured to perform the functions associated with the particularcircuitry as described herein. For example, in some embodiments,“circuitry” may include processing circuitry, storage media, networkinterfaces, input/output devices, and the like.

The terms “user” and/or the like may refer to an individual (e.g.,patient/member, physician, provider, provider team member, and/or thelike), business, organization, and the like. The users referred toherein may have user access privileges or user access levels for the ERsystem 100.

The terms “user group” and/or the like may refer to a group of users orindividuals, a group of businesses, a group of organizations, and thelike. The group of users referred to herein may have user group accessprivileges or user group access levels for the ER system 100.

The terms “access privilege,” “access level,” and/or the like may referto a variable whose value denotes a nature and/or extent of access by acomputing entity (e.g., a computing entity associated with a recognizedentity and/or user profile) to information/data associated with anexternal database. An access privilege may define which information/datastored by an external database a computing entity associated with arecognized entity may access. For example, a first access privilege mayenable a computing entity associated with a recognized entity to accessall information/data associated with one or more data objects within oneor more databases. As another example, a second access privilege mayenable a computing entity associated with a recognized entity to accessmetadata associated with one or more data objects within one or moredatabases. As a further example, a third access privilege may enable acomputing entity associated with a user account to access allinformation/data associated with the user account. As yet anotherexample, a fourth access privilege may enable a computing entityassociated with a user account to access metadata associated with theuser account.

The terms “concept” and/or the like may refer to ideas associated withsynonymous linguistic expressions of the concept. Each concept isdescribed in direct or indirect relationship to the root node of theontology, e.g., thing. A concept description must reference some anotherconcept to serve as its anchor and then specify or redefine additionalattributes (commonly referred to as “extra bits”) to distinguish it fromthe concept to which it is anchored. These attributes may specifymembership in additional sets (some) or specify constraint to specificvalues (min, max, value). Additionally the specification may specify theintersection of two or more descriptions (and) or the union of two ormore descriptions (or). In one embodiment, each incoming sourcevocabulary, source vocabulary code, and/or description can be convertedto an ontology concept and stored along with the originating vocabularyand identifier. Similarly, each outgoing ontology concept and identifiercan be converted to the originating (source) vocabulary and code.

The terms “compound concept” and/or the like may refer to a set ofconcepts that represent a single concept. A compound can be used tocreate a more granular concept (e.g., fracture of the left ulna).

The terms “ontology concept identifier” and/or the like may refer to aconcept identifiable via an ontology, such as an observable. Theontology concept identifier may comprise an alphanumeric code, such as2-3 uppercase letters forming a prefix, followed by a set of integersunique to that prefix.

The terms “ontology code” or “ontological code” is a machine-usable andmachine-readable term that represents a concept (e.g., left ulna,fracture, or facture of the left ulna). An ontological code can bemapped one-to-one to a corresponding concept.

The terms “code set” and/or the like may refer to a list of terms andtheir corresponding codes. A code set covers a defined knowledge domain,such as healthcare.

The terms “semantic network” is a set of concepts and a defined set ofnamed relationships. The semantic network expresses the known orrelevant relationships between concepts for a specific domain ofknowledge. A semantic network contains links that describe semanticrelationships among concepts.

The terms “observable” and/or the like may refer to a thing which can beobserved with an associated/corresponding value. Such things may bephysically perceivable and/or measurable elements. For example,eye-color may be an observable with an associated/corresponding value ofblue. Similarly, blood-pressure may be an observable with anassociated/corresponding of 140/80. In a particular embodiment, eachobservable is identifiable by an ontology concept identifier and hasassociated/corresponding value. Thus, each observable comprises akey-value pair (e.g., ontology concept identifier and itsassociated/corresponding value). FIG. 10A includes exemplaryobservations. An “observation” and/or the like may refer a thing thathas been observed with an associated/corresponding value.

The terms “observation group,” “observation groups,” and/or the like mayrefer to a collection of observables or observations related to oneanother as part of a single data object. The observables or observationsin the observation group are organized in a tree data structurerepresenting the relationship between information/data elements thatencapsulate the information/data intended to be stored in particulardata objects. And each observable or observation in an observation groupcomprises a key-value pair (e.g., ontology concept identifier and itsassociated/corresponding value). Further, the observations in theobservation group can be organized in a tree data structure representingthe relationship between data elements and which encapsulate theinformation/data intended to be stored in particular ER objects. FIG.10A includes exemplary observation groups.

The terms “portal,” “user interface,” and/or the like may refer toinformation/data encoding any combination of one or more visualrepresentations configured to be displayed to a user of a computingentity by a display device of the computing entity. In some embodiments,a user interface may include one or more visual representations thatrepresent a state of a system at a particular time. For example, atleast one of the one or more visual representations may render an emailcommunications resource transmitted to a computing entity of a useraccount by an email system on or before a particular time. As anotherexample, at least one of one or more visual representations may render agroup-based communication resource transmitted to a computing entity ofa user account by a group-based system.

The terms “interface display indication” and/or the like may refer toinformation/data transmitted to a computing entity that, when processed(e.g., executed) by the computing entity, causes the computing entity todisplay a user interface on a display device of the computing entity.For example, the interface display indication may be Hyper-Text MarkupLanguage (HTML) code configured to be executed by a web browser softwareframework executing on the computing entity. As another example, theinterface display indication may be information/data configured to beused by a mobile application software framework (e.g., an emailcommunication application software framework) executing on a computingentity to cause display of a user interface associated with the mobileapplication software framework.

The terms “user interaction event” and/or the like may refer to one ormore of transmission of a communication resource by a system to acomputing entity associated with a user account, transmission of acommunication resource from a computing entity associated with a useraccount to a system, and/or performance of an action by a user accountwith respect to a communication resource. Examples of user interactionevents include receiving communication resources, posting (e.g.,transmitting for rendering) communication resources (e.g., togroup-based communication channels), logging on to a system, activatinga user interface, launching an application, requesting access to a dataobject, sending communication resources, viewing body of communicationresources, viewing attachment files for communication resources,deleting communication resources, marking communication resources asspam, and/or the like.

The terms “past user interaction event” and/or the like may refer to auser interaction event whose timestamp indicates or is representative ofthat the user interaction event has occurred at a time prior toretrieval of information/data recording the user interaction event.

The terms “historical information/data” for a system may refer toinformation/data that represents an event whose timestamp indicates oris representative that the event has occurred at a time prior toretrieval of information/data.

The terms “natural language processing” and/or the like may refer to asoftware framework that processes natural language information/data(e.g., human language data, such as unstructured human language data) toextract one or more features from the natural language information/dataand/or to perform one or more predictions based at least in part on thenatural language information/data. For example, a natural languageprocessing module may use one or more syntactic processing techniquesand/or one or more semantic processing techniques. For example, anatural language processing module may use one or more of the followingtechniques: grammar induction, lemmatization, morphologicalsegmentation, part-of-speech tagging, parsing, sentence boundarydisambiguation, word segmentation, terminology extraction, lexicalsemantic determination, named entity recognition, optical characterrecognition, textual entailment recognition, relationship extraction,sentiment analysis, topic segmentation, word sense disambiguation,automatic segmentation, speech segmentation, text-to-speech conversion,and/or the like.

The terms “portlet” and/or the like may refer to user interfacecomponents that are managed and displayed in a web portal, dashboard,user interface, browser, and/or the like. Portlets can be pluggableaggregate (integrate) and personalize content from different sourceswithin a web page.

The terms “programmatic reasoning logic” may be a classifier that isresponsible for examining an ontology and inferring additionalrelationships based at least in part on the descriptions of the ontologyconcepts. In embodiments of the present invention, the programmaticreasoning logic may compare two arbitrary concepts described in thelanguage of the ontology and determine relationship. The programmaticreasoning logic may perform incremental reasoning, programmaticallyadding concepts to an existing ontology without having to recompute allinferences. The programmatic reasoning logic may also be able generateits internal graph directly from a pre-reasoned ontology.

The terms “tree data structure,” “container tree data structure,”“container tree,” and/or the like may refer to a non-linear datastructure where data objects are organized in terms of hierarchicalrelationships with a root value and subtrees of children with a parentnode, represented as a set of linked nodes. For example, in a tree datastructure, a node is a data object that may contain a value, condition,method for execution, reference, or represent a separate data structureor data object. The top node in a tree data structure may be referred toas the “root node.” A “child node” may be a node directly connected toanother node when moving away from the root node, an immediatedescendant. Similarly, a “parent node” may be the converse a child node,an immediate ancestor. A “leaf node” and/or the like may refer to a nodewith no children. An “internal node” and/or the like may refer to a nodewith at least one child. And “edge” and/or the like may refer to aconnection between one node and another node. The “depth” of a node mayrefer to the distance between the particular node and the root node.

The terms “subtree data structure” and/or the like may refer to datastructure that is descendant of the root node and itself has multipledescendants.

The terms “relationship,” “relations,” and/or the like may refer to anamed, directed (one way) association between two concepts. Arelationship establishes a semantic link between the respectiveconcepts. Any given concept may function as both the source anddestination of numerous relationships by establishing various namedrelationships. Thus, it is possible to have a variety of differentstructures—such as hierarchies, networks, and unstructured groups,superimposed on the same concepts. The ontology (represented as adirected acyclic graph data structure, a graph-based data structure,and/or the like) has the ability to be dynamically updated to modifyrelationships. Relationships are connections between objects (e.g.,nodes, entities (actors), such as primary care physician

patient, employer

employee, subscriber

payer, employer

payer, patient

service provider, and/or the like.

In one embodiment, the terms “direct relationship” and/or the like mayrefer to a relationship type in which two nodes are connected by oneedge. In another embodiment, the terms “direct relationship” and/or thelike may refer to a relationship type that is defined as being a directrelationship by two separate relationship data objects.

The terms “indirect relationship” and/or the like may refer to arelationship type in which two nodes are connected over a path throughat least one intermediate relationship nodes. In another embodiment, theterms “indirect relationship” and/or the like may refer to arelationship type that is defined as being an indirect relationship bytwo separate relationship data objects.

The terms “active relationship” and/or the like may refer to arelationship status in which there is or may be an ongoing interactionbetween two entities.

The terms “inactive relationship” and/or the like may refer to arelationship status in which there is not or is not expected to beongoing interaction between two entities.

The terms “traversal” and/or the like may refer to a tree traversal. Atraversal is a form of a graph traversal and indicates the process ofvisiting (checking, updating, executing methods, and/or the like) eachnode in a tree data structure, exactly once. Such traversals areclassified by the order in which the nodes are visited. Such traversalsinclude depth-first searches/traversals (e.g., preorder, inorder,postorder,), breadth-first searches/traversals, and/or the like.

The terms “data artifact packet data object,” “DAPDO,” and/or the likemay refer to a Java data object that is generated from an OPDO or anEPDO. In the context of an OPDO, the OPDO is transformed from an XMLdocument used for storage and transmission to a Java object that isexecutable or used for execution, such as via unmarshalling. Thus, theDAPDO comprises the structure of the OPDO with additionalinformation/data that allows for the construction of a container treedata structure to ingest information/data identified in the OPDO. In oneembodiment, each DAPDO comprises a DAPDO identifier, information/datafrom the corresponding OPDO, a DAPDO type (indicate the information/datait contains), an owner, a reference (e.g., a GUID) to the correspondingmessage, and a reference to the source system. In such a case, the DAPDOcomprises information/data from and/or metadata about the message. Themessage, for example, may be an HL7 message, and the metadata mayinclude the context of the message, e.g., the type of information/datain the message, the source system of the data, and the author of theinformation/data at the source system. In one embodiment, upon receiptof a message, a message data artifact packet data object isautomatically generated to store the message (e.g., message data object)and its corresponding metadata may be automatically generated. In thecontext of an EPDO, the EPDO is transformed from an XML, document usedfor storage and transmission to a Java object that is executable or usedfor execution, such as via unmarshalling. Thus, the DAPDO comprises thestructure of the EPDO with additional information/data that allows forthe construction of a container tree data structure to ingestinformation/data identified in the EPDO.

The terms “data artifact packet container,” “data artifact packetcontainer node,” “data artifact packet tree data structure,” “dataartifact packet container tree data structure,” “container tree datastructure,” “data artifact packet container tree,” and/or the like mayrefer to the tree data structure that comprises all other containers,container nodes, and/or the like. The data artifact packet containernode can define appropriate selectable observables to create therequested tree to be populated. In the OPDO context, each of thecontainer nodes in the built tree data structure comprises observationsused to populate data objects (in the EPDO context, tree data structurehas empty values for the corresponding observations). Similar to thetree data structure itself, such nodes are recursive but dependent upona defined hierarchical structure. For instance, an “actor” correspondsto an actor container node, and that actor's name corresponds to asubcontainer node of the actor container node, which is a type of actorname container. Actors may be people, places, organizations,locations—also referenced herein as entities.

The terms “containers,” “container nodes,” and/or the like may refer tonodes that aggregate related extractables, observables, or observations,such as those that pertain to a single object. For example, systolicblood-pressure and diastolic blood-pressure are related as a singleblood-pressure reading. Thus, each container node specifies a specificassemble method (e.g., assemble( ) method) and specifies the specificdata type as the return value (e.g., an <AssembledType> data object asits return value). The following is a non-limiting list of examplecontainer nodes an entity container node; entity name container nodewhich may have subcontainer nodes discrete entity name container and/orcomposite entity name container; address container which may havesubcontainer nodes discrete address container and composite addresscontainer; amount container; blob container; certification container;contract service plan container; contract services container; datacorrection container; data correction pairs container; email container;employment container; item (e.g., health item) container; health statusevaluation container node; health task container node; identifiercontainer node; language container node; language proficiency containernode; location container node; message container node; note containernode; observation element container node; occupation container node;plan descriptor container node; relationship container node; role fieldcontainer node; specialty container node; specimen container node;system task container node; system task schedule container which mayhave subcontainer nodes interval task schedule container and/orsingleton task schedule container node; telephone container which mayhave subcontainer nodes discrete telephone container and/or compositetelephone container node; temporal container which may have subcontainernodes ISO temporal container node; HL7 temporal container node;specified format temporal container node; test container node; valuecontainer node; website container node; and/or the like.

An Extensible Markup Language (XML) document (e.g., in an XML file) mayrefer a basic unit of XML information/data composed of elements andother markup in an orderly package. An XML document (e.g., in an XMLfile) may contains wide variety of information/data.

The terms “raw message,” “message,” “message data object,” and/or thelike may refer to a formal exchange of information/data, such as events,communications, notifications, requests, acknowledgements, replies,information/data exchanges, and/or the like. In the healthcare context,messages may be Digital Imaging and Communications in Medicine (DICOM®)messages, Health Level Seven International (HL7) messages, ElectronicData Interchange (EDI) messages, National Council for Prescription DrugPrograms (NCPDP) messages, X12 messages, XML messages, and/or the like.In one embodiment, messages may be processed and stored by the ER system100 may be stored as message “data artifact packet data objects”(DAPDOs), which are Java objects containing the relevant messageinformation/data (e.g., source of the message, author of the message,and/or the like).

The terms “application programming interface,” “API,” and/or the likemay refer to an interface or communication protocol between systems,components of a system, and/or the like. An API may be for a web-basedsystem, operating system, database system, computer hardware, softwarelibrary, and/or the like.

The terms “observable packet data object,” “OPDO,” and/or the like mayrefer to an object that is used as a data object to generate/create adata artifact packet data object from a message. In a particularembodiment, the OPDO is an XML, document (e.g., in an XML, file)specifying a set of observations organized into a hierarchy. Nesting inthe hierarchy represents repeated elements of the same type. In oneembodiment, each OPDO comprises an OPDO identifier.

The terms “observable packet data object identifier,” “OPDO identifier,”and/or the like may refer to an identifier that uniquely identifies atype of OPDO or a specific OPDO. In one embodiment, OPDO identifiers maybe UUIDs or GUIDs.

The terms “aggregators,” “aggregator nodes,” “aggregator containernodes,” and/or the like may refer to container nodes containing multipleelements into a single element. For example, an aggregate may aggregatea systolic blood-pressure reading (stored in a first data object) and adiastolic blood-pressure (stored in a second data object) into a singleblood-pressure reading.

Further, a container may represent a concept via a source vocabulary,vocabulary code, and description. An aggregator assembles these piecesinto a single ontology concept.

The terms “assemble,” “assembly,” “assemble method,” and/or the like mayrefer to a method for container nodes. For example, container nodesassemble( ) method and have an <AssembledType> data object as its returnvalue. To assemble, each observation in that container is individually,without respect to order, evaluated to identify the information/datait's value attribute holds. Once identified individually, theinformation/data can be assembled and evaluated for minimum requirementsin order to create a viable data object. In one embodiment, to preventrecursions and attempts to assemble container nodes with errors, duringassembly, a container exists in one of four states. The terms “notassembled” and/or the like may refer to assembly of a container that hasnot yet been attempted. The terms “undergoing disassembly” and/or thelike may refer to a container is currently being disassembled. The terms“assembled” and/or the like may refer to a container has beensuccessfully assembled. The terms “unable to assemble” and/or the likemay refer to the attempt to assemble a container that has resulted in anexception.

The terms “extractable,” “retrievable,” and/or the like may beconsidered the equivalent of the observables. That is, extractablesrepresent data elements stored with respect to a specific actor thatwere stored as observables or observations. Extractables may representeither singletons or collections. For example, theassociated/corresponding value of eye-color may be extracted. Similarly,the associated/corresponding value of blood-pressure may be extracted.In a particular embodiment, each extractable is identifiable by anontology concept identifier.

The terms “extractable packet data object,” “EPDO,” and/or the like mayrefer to an object that is used as a data object to generate/create adata artifact packet data object that identifies information/data forextraction (e.g., retrieval). In a particular embodiment, the EPDO anXML document (e.g., in an XML, file) specifying a set of observationsorganized into a hierarchy. Nesting in the hierarchy represents repeatedelements of the same type.

The terms “extractable packet data object identifier,” “EPDOidentifier,” and/or the like may refer to an identifier that uniquelyidentifies a type of EPDO or an EPDO. In one embodiment, OPDOidentifiers may be GUIDs.

The terms “fit,” “accurate,” “best,” “better,” and/or the like may referto information/data that is considered the most recent, most specific,from the most reliable source, and/or the like. Thus, the SBROprocessing module 265 information/data fitness determines what the“best” information/data. For example, if an existing SBRO object isstoring Joe Smith's date of birth as Jan. 3, 1955, but the ER system 100then receives a message identifying his date of birth as Jan. 3, 1956.The SBRO processing module 265 determines whether the newinformation/data is more “fit,” “accurate,” “better,” than the existinginformation/data. The decision is encapsulated in a SBRO processingmodule 265 that considers various factors specific to the type ofelement, such as (1) which version is newer, (2) which version comesfrom the more reliable source, and/or (3) which version is the morespecific/complete. It is also important to understand that an SBROexists as a collection of individual elements and that information/datafitness is applied to the individual elements rather than to the SBRO asa whole. Continuing the above example, suppose that we also believedthat Joe Smith's weight was 160 lbs. and then within the same recordthat we were informed of the alternate birth date we were also informedthat his weight was really 155 lbs., it is perfectly possible (thoughperhaps unlikely) to accept the new birth date while rejecting the newweight. This is because each element is evaluated independentlyevaluated for fitness based at least in part on its own merits. Thus theoverall SBRO represents the accumulated best knowledge of a set ofindividual elements.

The terms “change log” and/or the like may refer to changes of a subjectentity's ER logged in a specific table or database. For example, achange table may comprise data objects storing two attributes: a pointerto the modified actor and the time of the log entry. The change log isused by the information/data extract Processor.

The terms “container tree generation” for extractables may refer to theextraction processing module 260 generating a tree data structurecomprising based at least in part on a data artifact packet containernode and its subcontainer nodes EPDO (that initially contains no valuesor empty values (e.g., values that are not yet populated)). This treedata structure is a representation of the relationships betweeninformation/data elements within the ER system 100 and functions as theframework for aggregating information/data element pieces retrieved fromthe ER system 100.

The terms “disaggregators” and/or the like may refer to methods thatdecompose a particular ER element into multiple observations. Forexample, during ingestion processing, an incoming source vocabulary,vocabulary code, and optionally description can be converted to an ERontology concept, which is stored along with the originating vocabularyand code. During extraction processing, the requesting entity istypically interested in the original vocabulary and code along with theER information/data. Thus, a disaggregator produces the followingtriple: (1) ontology concept identifier, the originating sourcevocabulary concept identifier (if present), and the originating sourcevocabulary code (if present). Thus, when converting the container treedata structure to an observation group, the extraction processing module260 includes the triple in a child observation group.

The terms “disassemble,” “disassembly,” “disassemble method,” and/or thelike may refer to a polymorphic method (e.g., a method that may, atdifferent times, invoke different methods) for populating containernodes. Disassembly is the process by which the extraction processingmodule 260 acquires values for the extractables specified in anobservation group and populates in the values in each container node ofthe container tree data structure. This process is referred to asdisassembly and is the inverse of the ingestion processing module 225assembly function. For example, the disassemble method acquires thevalue for the extractable represented by that container node. Acontainer node either represents a specific element of information/dataor it has subcontainer nodes that represent parts of the valuerepresented by the container. The disassembly process is the inverse ofthe process performed by the ingestion processing module 225 via theassembly function. In one embodiment, to prevent recursions and attemptsto disassemble container nodes with errors, during disassembly, acontainer exists in one of four states. The terms “not disassembled”and/or the like may refer to disassembly of a container that has not yetbeen attempted. The terms “undergoing disassembly” and/or the like mayrefer to a container is currently being disassembled. The terms“disassembled” and/or the like may refer to a container has beensuccessfully disassembled. The terms “unable to dissemble” and/or thelike may refer to the attempt to disassemble a container that hasresulted in an exception or there is no persisted information/data todisassemble. In one embodiment, a container tree data structure ispopulated by invoking getDisassembledObject( ) on the DAPDO at the rootnode of the container tree data structure. The invocation initiates acascade with each subcontainer node similarly evaluated down to the leafnodes of the container tree data structure through which the containertree data structure is populated. When an extractable (or group ofextractables) represents a collection, the extraction processing module260 returns all elements of the collection in an observation group, witheach element of the collection in its own child observation group. Thus,acquiring values for container nodes consists either ofextracting/retrieving a value directly from the actor's ER or inaggregating the values from subcontainer nodes. There may also beadditional observations to be populated in a particular container inaddition to the persisted ER information/data.

The terms “queue” and/or the like may refer to a sequence of objectsthat are waiting to be processed.

The terms “durable storage” and/or the like may refer to a database thatpersists in spite of software failures, system shutdown, and withcaveats, hardware failures.

The terms “persistence manager” and/or the like may refer to a classwhose responsibility it is to create, read, and update informationrecords and delete database records (e.g., DBOs). A persistence manageris generated for each entity class and database in the DSML 245.

As noted elsewhere, the terms “database” and/or the like may refer towhere the program saves objects. Thus, the term database does not implyany particular kind of database as a system utilizing this program coulduse one of many products to store objects. For example, the databaseused could include Cassandra DB, PostgreSQL, Berkeley DB, etc.

The terms “transaction” and/or the like may refer to a group ofpermanent object operations that are grouped into a logical unit ofwork. Either all of the transaction's operations are applied to thedatabase as an atomic unit or no operation is applied.

The terms “transaction cache” and/or the like may refer to a structurethat tracks which entity objects are created, read, updated, or deletedduring the course of a particular transaction.

The terms “class” and/or the like may refer to a Java class orinterface.

The terms “object” and/or the like may refer to a Java object. When anobject of a particular class is referenced, the class's name may be usedin place of the word “object” altogether. When several objects of thatclass are relevant, the plural form of the class's name may be used.Unless noted otherwise, if an object is said to be an instance of aparticular class, the object may also be an instance of any subclass ofthat class.

The terms “subdatabase,” “sub-database,” and/or the like may refer to aportion of the database which stores instances of a single DBO class. Asubdatabase is created for every entity class in the DSML 245.

The terms “database record,” “database object,” “DBO,” and/or the likemay refer to an object that is specific to a particular kind of durablestorage with the proper knowledge to interact with the correspondingpersistence manager.

The terms “health item” and/or the like may refer information/data thatcorresponds to an instance of a health condition. In various scenarios,the health condition affects a person's health for a finite period oftime (e.g., a broken bone, a stomach bug, and/or the like). In suchscenarios, each instance of the health condition corresponds to adifferent health item. In various scenarios, the health condition mayaffect a person's health for an indefinite period of time (e.g.,diabetes, epilepsy, and/or the like) and a single health item maycapture evolving information/data corresponding to the health condition.

The terms “index,” “database index,” and/or the like may refer to a datastructure that improves the speed of information/data retrievaloperations on a database table. Indexes allow for the efficient locationof information/data without having to search every row in a databasetable, for instance, every time a database table is accessed. Suchindexes may include clustered indexes, non-clustered indexes, and/or thelike. The indexes may be binary trees and/or the like.

The terms “collection” and/or the like may refer to a grouping of somevariable number of data items (possibly zero) that have sharedsignificance to a particular function, process, and/or the like, andthat should be operated on together in some controlled fashion. Forexample, in various embodiments, in addition to storing DBOs, the DSML245 is capable of storing two kinds of persistent collections as fieldsof DBOs. The two types of collections that the data store managementlayer is capable of storing, in various embodiments, are bags and lists.A bag is a collection that does not support ordering or indexing, and insome embodiments, which permits duplicate entries. A list is acollection that supports ordering and indexing, and in some embodiments,which permits duplicate entries.

The terms “primary key” and/or the like is a list of objects used tofind an object's database record. The key may include an object's classand/or an integer that links an object to its database record. This keyis generally a part of an object's metadata.

The terms “entity class,” “entity object,” and/or the like may refer toa database record with a particular primary key.

The terms “embedded class,” “embedded object,” and/or the like may referto a database record that is part of another DBO.

The terms “persistent flag” and/or the like may refer to a state fieldindicating whether the current object has been written to durablestorage.

The terms “persistent object” and/or the like may refer to a databaseobject in durable storage.

The terms “persistent collection” and/or the like may refer to acollection in durable storage.

The terms “connectoid” is a data object that indicates a relationshipbetween two ontology concepts. In the graph data structurerepresentation of the graph-based domain ontology, a connectoid isrepresented by an edge of the graph. In various embodiments, theconnectoid may be associated with a direction (e.g., unidirectional orbi-directional), with a relationship type, and/or the like.

The terms “modified flag” and/or the like may refer to a state fieldindicating whether the current object has changed since durable storagewas last synchronized with persistent objects.

The terms “modified collections” and/or the like may refer to a set thatcontains all of an instance's modified standard collections.

The terms “modified collection set” and/or the like may refer to a setthat contains references to child collection proxies that have beenmodified since the last synchronization.

The terms “commit” and/or the like may refer to the termination of atransaction in the database system that makes durable and visible toobservers the effects of the transaction's operations.

The terms “flush” and/or the like may refer to a command used tosynchronize durable storage with the state of persistent objects in adatabase at that point.

The terms “entity proxy,” “entity proxies,” and/or the like may refer toan object with the necessary information to load an entity object. Thisproxy is used in place of the object to delay the loading of someobjects until they are requested.

The terms “collection proxy,” “collection proxies,” and/or the like mayrefer to a collection whose primary purpose is to delay the loading ofCollection elements until requested.

The terms “health item” and/or the like may refer to drugs, procedures,goals, medications, conditions, tests, ailments, or other relatedobjects/terms but not including actors or relationships.

The terms “collection” and/or the like may refer to a grouping of dataitems with a shared significance. The use of this term does not denoteany particular type of collection.

In the context of the data store, the terms “entity” and/or the like mayrefer to an independent item of which information or data can be storedin a database. In one embodiment an entity may comprise variouselements, attributes, or parts. However, outside of the data storecontext, the terms “entity,” “actors,” “person actors,” “organizationactors,” “location actors,” and/or the like. Thus, entities, subjectentities, requesting entities, and/or the like refer to these types of“actors,” and the reverse is also true.

The terms “DBO class” and/or the like may refer to a class which isdefined in such a way that the DSML 245 is able to manage the lifecycleof its instances.

The terms “primary index” and/or the like may refer to a mapping fromprimary keys to the DBOs of a subdatabase. There can only be one primaryindex for each DBO class.

The terms “user role” and/or the like may refer to a role given to anentity of which is assigned one or more rights groups.

The terms “rights group” and/or the like may refer to a group of whichcomprises one or more rights defined in a rights group data object, suchas an XML document. The rights provide the entity the ability to accessthe calling of particular functions/operations.

The terms “owner” and/or the like may refer to the person, individual,actor, and/or entity who owns the information/data in an ER associatedwith a subject entity identifier that corresponds to the owner. In otherwords, the subject entity associated with the ER, is the owner of theinformation/data stored in the ER.

The terms “owner roles” and/or the like may refer to a role that allowsthe owner of an ER to control who has access to what information/dataand/or who can perform what functions with regard to theinformation/data stored in the ER corresponding to the owner.

The terms “requester role” and/or the like may refer to arequestor/requesting entity/acting entity is a user/entity requestingthat a function be performed to an ER stored in the data storage of theER system 100. The requestor role is the user role corresponding to therequestor based at least in part on the relationship between therequestor/requesting entity/acting entity and the subject entity of theER the requestor/requesting entity/acting entity is requesting to actupon.

The terms “scope access,” “population access,” and/or the like may referto who can access information/data (e.g., sensitive, protected, authoredprotected, and/or the like).

The terms “data access” may refer to which information/data a user(e.g., entity may access).

The terms “operation access” and/or the like may refer to whichfunctions and/or operations a requesting/acting entity may perform(and/or cause the performance of) on an ER corresponding to a subjectentity. In various embodiments, the operations access of arequesting/acting entity corresponding to a particular ER correspondingto a subject entity is determined based at least in part on the rightsgroup(s) assigned to a user role that matches the user role of therequesting/acting entity in the relationship between therequesting/acting entity and the subject entity.

The terms “ontology class” and/or the like may refer to a conceptdefined within the graph-based domain ontology. In various embodiments,an ontology class is associated with an ontology concept identifier, oneor more relationships indicating how the ontology class is related toone or more other ontology classes and/or ontology concepts, and/or thelike.

The terms “ontology category” and/or the like may refer to a conceptthat is not defined in the graph-based domain ontology. In particular, acategory may be identified (e.g., in a message) that is distinct and/ordifferent from each ontology class and/or concept defined by thegraph-based domain ontology. In various embodiments, programmaticreasoning logic may be used to determine and/or reason relationshipsbetween a category and one or more ontology classes and/or concepts.

IV. EXEMPLARY SYSTEM OPERATION

Various embodiments provide methods, apparatus, systems, computerprogram products and/or the like for automatically managing, ingesting,monitoring, updating, and/or extracting/retrieving information/data inan ER data store (e.g., database) and/or providing and/or controllingaccess to information/data stored in the ER data store (e.g., database)based at least in part on a graph-based domain ontology. As previouslynoted, the term “ontology” and/or the like may refer to a recursive datastructure comprising various nodes (e.g., a directed acyclic graph datastructure, a graph-based data structure, and/or the like). In oneembodiment, the ultimate root node of the graph-based ontology is“thing.” The root node can be divided into various configurable levels(e.g., conditions, devices, and/or the like). For example, FIG. 5A showsthe root of the graph-based ontology with configurable levels (and someexemplary description logic). Each configurable level may be a “domainof interest” or a “domain of knowledge.” In a particular embodiment, thegraph-based ontology is a terminological component (TBox) ontology.Ontologies represented as directed acyclic graph data structures havevarious technical benefits, including improved interoperability,improved reasoning, reduced errors, improved information/data mining,improved analytics, and/or the like.

As will be recognized, an ontology (in the context of computerizedinformation systems) is a formal way of representing what things are orwhat they mean. The graph-based ontology is used to represent conceptsin a graph-based data structure. The concepts are then used to store theinformation/data via an ER data store (e.g., database). In thegraph-based ontology, description logic uses formal descriptions torepresent concepts. These descriptions (e.g., class expressions) areconstructed from less complex class expressions and relationships usingformal operations such as AND, OR, SOME, and/or ALL. This process isanalogous to how more complex phrases are constructed in naturallanguage using simpler phrases, words, and grammar. The descriptionlogic language, however, has formal rigor given by a preciseinterpretation of the operators (AND, OR, and/or the like) that allowscomputing entities to process the information/data.

FIG. 4 shows a portion of an ontology concept (Tenoretic 50) representedas being constructed from things such as the active ingredients, theformulation, dose strength, route of administration, and/or the like.Thus, all the constituent parts are independently accessible tocomputing entities so they can execute useful actions with the formalmeaning.

In various embodiments, the concepts defined within the graph-basedontology (referred to herein as concepts or classes) are defined, atleast in part, on the relationships between and among various classes.For instance, in various embodiments, the graph-based ontology isrepresented by a graph-based data structure. FIG. 5B illustrates oneslice through a medications portion of the graph-based data structurerepresented in the graph-based domain ontology. The illustratedgraph-based data structure represents one slice through medicationsuseful for obstructive breathing conditions, including not onlyalbuterol, but also inhaled corticosteroids, methylxanthines, and/or thelike. Each medication has its own different slice (in a differentdimension), not shown here. In this dimension, a number of dose anddelivery forms are shown for albuterol, but in a different view, similar“explosions” exist for each of the short-acting and long-actingbronchodilators shown in the figure. And similar explosions of differentkinds of medications could be made for the steroids, xanthines, and/orthe like.

Similarly, FIG. 5C shows a portion of an ontology concept forrespiratory system disorders, some of which are lung disorders, some ofwhich are obstructive, some of which are COPD. Each of these may have arange of severities and incorporates the “older” terms of emphysema andchronic bronchitis—both now subsumed to COPD. As previously noted, eachconstituent part is independently accessible to computing entities sothey can execute useful actions with the formal meaning along with therelationship paths. FIG. 6 illustrates a portion of a definition of anontology concept for obstructive lung disease.

In one exemplary embodiment, the graph-based ontology may comprise anydiscrete number of vocabulary terms, referencing a set number ofdistinct concepts represented as ontology concept identifiers. There arerelationships among the graph-based ontology concept identifiers asrepresented by the graph-based data structure that can become moremature and meaningful as additional use cases are examined andincorporated into the ER system 100. Concepts can be mappedbidirectionally to and from various source vocabularies.

As noted above, embodiments of the present invention provide technicalsolutions to technical problems related to the automated managing,ingesting, monitoring, updating, and/or extracting/retrieving ofinformation/data of a data store (e.g., a database or other data store)and/or accessing information/data from the automatically managed andupdated data store 211. In particular, the use of the graph-basedontology to define concepts and relationships between and among conceptsenables the ER system 100 to interpret information/data received by theER system 100 (e.g., via messages provided by source systems 40) suchthat multiple messages corresponding to the same event and/ortransaction may be identified, even when the multiple messages refer tothe event and/or transaction using disparate vocabularies. Moreover, theinformation/data provided by the multiple messages and referring to thesame event and/or transaction may be combined so as to provide a singlebest collection of information/data (e.g., an SBRO) corresponding to theevent and/or transaction. Embodiments of the present invention thereforeovercome the technical problems of identifying messages of disparatevocabularies that correspond to a same event and/or transaction anddistilling those identified messages down to a single best collection ofinformation/data corresponding to the event and/or transaction in anautomated fashion. Embodiments of the present invention thereforeprovide improvements in the field of automated data store managing,ingesting, monitoring, updating, and/or extracting/retrieving ofinformation/data.

General Overview

In various embodiments, to manage and/or update the database, a messageis generated/created and provided by a source system 40 such that theserver 65 receives the message via one or more communicationsinterfaces, as shown in FIG. 7A. The message may be in a source formatand/or source vocabulary that is used by the source system 40. Theserver 65 may pre-process the message (e.g., by the messagepre-processing module 220 operating on the server 65) to translate theinformation/data from the message into a normalized format and into anontology vocabulary. The graph-based ontology vocabulary comprises classand/or ontology concept identifiers corresponding to concepts defined inthe graph-based ontology. In an example embodiment, the graph-basedontology vocabulary further comprises descriptions corresponding toconcepts of the graph-based ontology. In one embodiment, an OPDO is adata object generated/created that comprises the meaningfulinformation/data from the message in the normalized format and in thegraph-based ontology vocabulary. The result of pre-processing themessage may vary. In one embodiment, the result of the pre-processing anOPDO. In another embodiment, the result of the pre-processing is aDAPDO. One or more entities, organizations, locations, individuals,and/or the like referenced in the message may be identified (e.g., by anidentity matching service 240 operating on the server 65). The DAPDO maybe updated to include entity identifiers configured to identify the oneor more entities, organizations, locations, individuals, and/or the likewithin the ER system 100.

The OPDO or DAPDO may then be provided to the ingestion processingmodule 225 for processing. In one embodiment, the OPDO or DAPDO may beprovided to the ingestion processing module 225 with the entityidentifiers (e.g., in real-time or near real-time with respect to thegeneration of the OPDO or DAPDO and/or updating of the OPDO or DAPDO toinclude the entity identifiers). In another embodiment, the DAPDO isstored for deferred processing (e.g., user interaction event processing)and/or prioritized processing. For example, DAPDOs may be processed at alater time responsive to a trigger being identified based at least inpart on user interaction event (e.g., a user logging into the ER system100) and/or based at least in part on a priority scheme. In variousembodiments, the ingestion processing module 225 processes the DAPDO togenerate/create one or more data transfer objects that are then used toupdate the ER data store 211 via a single best record object (SBRO)process, which may include the use of programmatic reasoning logic 230.A DSML 245 may be used as an interface between the ER data store 211 andthe ingestion processing module 225, SBRO processing module 265, and/orother programs, processes, modules, engines, and/or the like of the ERsystem 100. For instance, in various embodiments, the ingestionprocessing module 225, SBRO processing module 265, and/or various otherprograms, processes, modules, engine, and/or the like of the ER system100 are configured to process data objects (e.g., Java objects and/orthe like). The ER data store 211 may be a database or other data storethat does not support direct storage of data objects. The DSML 245 maytherefore act as an intermediary and/or interface for persisting/storingand/or extracting/retrieving the data objects in the ER data store 211.

In various embodiments, after and/or responsive to one or more dataobjects of the ER system 100 being updated based at least in part on theprocessing of one or more messages, a rules engine 250 may analyzevarious ERs to determine if the at least one of the ERs satisfies one ormore conditions. In various embodiments, when the at least one of theERs triggers an action (e.g., by satisfying or not satisfying one ormore conditions in accordance with rules applied by the rules engine250), the rules engine 250 generates an OPDO, and the OPDO is providedto the ingestion processing module 225 for processing, such that the ERdata store 211 is updated based at least in part on the triggering ofthe action.

In various embodiments, a user may wish to access information/datastored in the ER data store 211. FIG. 7B provides a schematic diagramillustrating components of the ER system 100 that may be used to receivea request to access information/data stored in the ER data store 211 andto provide a response to the request. In various embodiments, the usermay operate a user computing entity 30 to access an online portal and/orother interactive user interface (IUI). Via the online portal and/orother IUI, the user may cause the user computing entity 30 togenerate/create and provide a request for information/data such that therequest is received by the server 65. The server 65 may access whetherthe user has appropriate user rights to access the requestedinformation/data (e.g., via the data access/function controller 255operating on the server 65). After and/or responsive to determining thatthe user has appropriate user rights to access the requestedinformation/data, the extraction processing module 260 may process therequest and access the requested information/data from the ER data store211. In various embodiments, the DSML 245 may act as an intermediaryand/or interface between the extraction processing module 260 and the ERdata store 211. The computing entity may generate/create a responsecomprising the accessed information/data in a format and languagecorresponding to the online portal and/or other IUI and/or the usercomputing entity 30 that generated/created and provided the request.

Various components of the ER system 100 will now be described in furtherdetail.

Message Pre-Processing

In one embodiment, each message, regardless of source, undergoespre-processing. The pre-processing may include validating, filtering,parsing, and transforming/normalizing the message and/orgenerating/creating corresponding OPDO or DAPDO. In one embodiment, theER system 100 may comprise various communications interfaces forreceiving, filtering, transforming, storing, and/or the like messages.For example, the ER system 100 may receive DICOM® messages, HL7messages, EDI messages, NCPDP messages, X12 messages, XML messages,and/or the like via a variety of communications interfaces. Forinstance, various source systems 40 may generate/create messages andprovide the messages such that the server 65 of the ER system 100receives the messages (e.g., via the communications interfaces). Theseinterfaces may allow for interaction and communication with disparatesource systems 40 using different technologies and protocols, includingFile Transfer Protocol (FTP), SFTP (SSH File Transfer Protocol), File,Hypertext Transfer Protocol (HTTP), Java Message Service (JMS), LowerLayer Protocol (LLP), Open DataBase Connectivity, Transmission ControlProtocol (TCP), Simple Object Access Protocol (SOAP), JavaScript, and/orthe like. Transformation tools include, but are not limited to, Java,JavaScript, Tcl, Python and Extensible Stylesheet LanguageTransformations (XSLT).

FIG. 8 provides a flowchart of exemplary process 800 (e.g.,pre-processing and/or transformation process 800) illustrating variousprocesses, procedures, steps, operations, and/or the like performed, forinstance, by a message pre-processing module 220 operating on and/orbeing executed by a server 65. In various embodiments, thepre-processing and/or transformation process 800 is configured togenerate/create an OPDO and/or DAPDO for each message (e.g., one OPDOand one DAPDO for each message) based at least in part on incomingmessages, such that OPDOs or DAPDOs may be processed by the ingestionprocessing module 225.

Starting at step/operation 802, a message is received and assigned amessage identifier. The message identifier allows the message to bestored in the system for various purposes. For example, the server 65may receive a message via the communications interface 208, and/or thelike. For instance, a source system 40 may generate/create a message andprovide (e.g., transmit) the message such that the server 65 receivesthe message via a communications interface. In various embodiments, themessage may be provided in a source vocabulary and/or source format. Forexample, the source system 40 may generate/create the message using asource vocabulary and/or source format that is used by the source system40, for example, for internal processes. Upon receipt of the message,the communications interface 208 and/or processing element 205 may passthe message to the message pre-processing module 220.

Upon receipt of a message, the processing element 205 validates,filters, parses, and transforms/normalizes the message (e.g.,information/data stored therein) as part of generating/creating an OPDOor a DAPDO for the message. As part of this process, the message isfirst validated and filtered at step/operation 804. For example, thevalidation may be a syntactic validation, e.g., to ensure that a givenmessage is complete and properly formed. Similarly, the filtering mayidentify messages that are high priority messages. For instance, rulescan detect high priority messages to be submitted to the ingestionprocessing module first. Then, the message is parsed into discrete datafields at step/operation 806. For instance, the message 900 shown inFIG. 9 shows a flat message (e.g., HL7 message) with delimited fields.In one embodiment, each of the following are parsed into discretefields: PHNL160122, 299668, PATIENTIFIRST, PATIENTLAST, 1948-08-2300:00:00.000, M, MEDICARE, 968887387, 1972697498, 2018-02-0100:00:00.000, 2018-03-31 00:00:00.000, MEDICARE. After parsing themessage, the parsed information/data is transformed or normalized intoontology concepts.

At step/operation 808, information/data in the messages istransformed/normalized from the source vocabulary to the ontologyvocabulary. Source vocabularies may include a number of code systems andsets, such as ANSI X.12 (standard for defining electronicinformation/data exchange of healthcare administrative transactions);ANSI HL7 versions 2 and 3 (standards for the exchange, management andintegration of electronic healthcare information/data); CPT (CurrentProcedural Terminology); HCPCS (Healthcare Common Procedure CodingSystem); ICD-9-CM and ICD-10 (International Classification of Diseasesand Procedures); ISO (Internal Standards Organization); LOINC (LogicalObservation Identifiers, Names and Codes); NACIS (Northern AmericanIndustry Classification System); NCPDP (script ePrescribing standard);NDC (National Drug Codes); NUBC (National Uniform Billing Code); RxNom(nomenclature for clinical drugs); and SNOMED CT (SystematizedNomenclature of Medicine), and/or the like. To transform/normalize fromthe source vocabulary to the ontology vocabulary, the discrete datafields are mapped from the source vocabularies to ontology vocabulary(e.g., ontology concepts), such as “diagnosis” or “procedure.” Inparticular, each discrete data field is transformed/normalized to anontology concept and is assigned the corresponding ontology conceptidentifier. Continuing with the above example, the following areassigned ontology concept identifiers: PHNL160122>>0X21265,299668>>0X21537, PATIENTIFIRST>>0X211, PATIENTLAST>>0X254, 1948-08-2300:00:00.000>>0X9904, M>>0X12518, MEDICARE>>OX 21536, 968887387>>OX21261, 1972697498>>0X21272, 2018-02-01 00:00:00.000>>0X21324, 2018-03-3100:00:00.000>>0X21323, MEDICARE>>0X21536, and/or the like. As will berecognized, this approach allows for the translation and normalizationof the information/data. Further, each ontology concept identifier isdefined within the graph-based ontology and indicates the relationshipand attribute information/data for the corresponding concept (see FIGS.5A, 5B, AND 5C). This allows the ER system 100 to automatically andprogrammatically understand each information/data element, including itsintegration in the graph-based ontology. For example, if a person has aCaesarean section, the ER system 100 (using the graph-based ontology andontology concept identifiers) can automatically and programmaticallyinfer that the individual is a female, has been pregnant (gravida>0),and has had a non-vaginal delivery of a child, and/or the like.Similarly, the directed acyclic graph structure of the graph-basedontology also can be used to represent inheritance classes. Forinstance, an elevated Hemoglobin A1C indicates that the individual hasdiabetes, but also the inheritance of an entire class of characteristicsof people that have diabetes.

After translating/normalizing from the source vocabulary to the ontologyvocabulary, an OPDO is generated/created based at least in part on an“observable packet definition” for the message, which iscommunications-interface specific (step/operation 810). The observablepacket definition uses the parsed and transformed/normalizedinformation/data to generate/create an OPDO comprising theinformation/data values and their associated observables. Additionally,corresponding event/activity sets provide the context of theinformation/data comprised in the messages. Each event/activity has itsown set of observables uniquely related to its context (a one to manymapping). For example, the context of a subject entity who is thesubject of an eligibility event/activity message is an “insured” person,whereas the context of a subject entity who is the subject of a visitevent/activity message is a “patient.” The event/activity selected for agiven interface definition limits the observables available for datafield-defining, so only those concepts with the appropriate context areavailable for selection. Thus, the definition of an observable comprisesthe event/activity to which it is related, the type of entity itobserves, the role of that entity, the aspect of the entity observed,the data type that describes the format of the information/data, and/orthe like.

In various embodiments, generating/creating an OPDO comprisesidentifying observation groups from the observations provided in themessage. As described above, an observation comprises a conceptidentifier (e.g., an observable identifier) and a corresponding value(e.g., the value determined and/or measured when the observable wasobserved). An observation group is a collection of observations that arerelated to one another as part of a single object. For example, adiastolic blood pressure measurement and a concurrently capturedsystolic blood pressure measurement are each observations that may bedetermined to be an observation group corresponding to blood pressure.In various embodiments, the observations may be organized into ahierarchical within the observation group, such that the relationshipsbetween observations within the observation group are indicated by thehierarchy. FIG. 10A illustrates an example OPDO 1000. In variousembodiments, an OPDO is an XML document. As seen from the OPDO 1000,this particular OPDO 1000 comprises both observations and observationgroups. After generation/creation of an OPDO, processing can continue toeither step/operation 812 or step/operation 820.

If continue to step/operation 812, the message 900 may identify or referto one or more entities. As will be recognized, the message 900 maycomprise attributes corresponding to various entities that have varioususer roles corresponding to the event and/or transaction associated withthe message 900. For example, the message 900 may reference a subjectentity who is the subject of the event and/or transaction correspondingto the message. For instance, if the message 900 corresponds to amedical/healthcare event and/or transaction, the subject entity may be apatient receiving treatment, care, therapy, medication, advice, testing,and/or the like referenced in the message 900. Continuing with themedical/healthcare event and/or transaction example, the message 900 mayfurther reference a treating physician, a lab technician who measuredbiometric information/data of the patient, a phlebotomist who drew bloodfrom the patient, and/or the like as appropriate for the event and/ortransaction corresponding to the message 900. At step/operation 812,attributes corresponding to entities referenced in the message 900 maybe determined, identified, and/or the like. In various embodiments, theattributes corresponding to entities that are provided by the message900 is dependent on the source system 40 that generated/created themessage. For example, if the source system 40 is operated by and/or onbehalf of a physician, the message 900 may comprise different, thoughpossibly overlapping, attributes corresponding to entities than if thesource system 40 is a claims processing system.

FIG. 11A illustrates some exemplary attributes 1102 that may bedetermined and/or identified for an entity (e.g., person actor,organization actor, location actor). In various embodiments, anattribute 1102 comprises (1) an attribute type (e.g., Medicare healthinsurance claim number (HICN), Medicaid recipient ID, healthcare ID,national provider identifier (NPI), medical record number), (2)attribute value (e.g., an alphanumeric code providing a valuecorresponding to the attribute type), and (3) an assigning authority(e.g., the source assigning attribute values for the particularattribute type). Information from a message 900 and/or an OPDO 1000 maybe analyzed to identify attributes corresponding to various entitiesreferenced in the message 900. As will be recognized, attributes may beother pieces of information/data, such as gender, birthdate, name,address, relationships to one or more known entities, and/or the like.For instance, panel 1104 of FIG. 11B identifies multiple entities (e.g.,entities having different roles) corresponding to the event and/ortransaction, as shown by the double underlined terms. Attributescorresponding to these entities are determined. For example, theattribute type is shown in all capitals, and the corresponding attributevalues are shown singly underlined in panel 1104. And for a givenmessage, there may be any number of entities.

Returning to FIG. 8, at step/operation 814, an identity matching service240 is invoked to identify an entity (e.g., person actor, organizationactor, location actor) known to the ER system 100 based at least in parton the attributes corresponding to the entity determined from theprocessing of the message (e.g., via process 1200 of FIG. 12). Forinstance, the message pre-processing module 220 may generate/create andprovide an invoking API call (e.g., an API request) passing one or moreattributes corresponding to an entity referenced in a message to theidentity matching service 240. At step/operation 816, an entityidentifier corresponding to the entity may be received via an APIresponse. For example, the identity matching service 240 may identify anentity (e.g., person actor, organization actor, location actor) known tothe ER system 100 based at least in part on the one or more attributescorresponding to the entity and passed to the identity matching service240, and may return an API response comprising an entity identifier forthe entity (e.g., person actor, organization actor, location actor). Invarious embodiments, the message pre-processing module 220 maygenerate/create and provide a plurality of invoking API calls (e.g., APIrequests) or a composite API call, such that each entity referenced inthe message may be identified. For instance, an entity identifier may bedetermined for each of a plurality of entities referenced in themessage. As will be recognized, process 1200 of FIG. 12 could occurbefore, during, as part of, and/or after the generation of the OPDO orthe DAPDO. Although steps/operations 812, 814, and 816 are described inthe context of the message pre-processing module 220, thesesteps/operations may also be executed by the ingestion processing module225 in certain embodiments.

At step/operation 818, a DAPDO is generated/created based at least inpart on the OPDO and other information/data, such as information/data inthe message referenced by message identifier. For example, the OPDO(along with other information/data) is transformed from an XML, documentused for storage and transmission to a Java object that is executable orused for execution, such as via unmarshalling. Thus, the DAPDO comprisesthe structure of the OPDO with additional information/data that allowsfor the construction of a container tree data structure to ingestinformation/data identified in the OPDO. For example, each DAPDOcomprises a DAPDO identifier, information/data from the correspondingOPDO, a DAPDO type (indicate the information/data it contains), anowner, a reference (e.g., a GUID) to the corresponding message, and areference to the source system. This allows the DAPDO to begenerated/created with the necessary information/data. Continuing withthis example, the message pre-processing module 220 may generate/createa DAPDO for each received message. Generally, for each message, the ERsystem 100 generates one DAPDO. The DAPDO typically comprisesinformation/data (including metadata) from the message and is structuredbased at least in part on the corresponding OPDO that was previouslygenerated. In various embodiments, the metadata for the message maycomprise a source identifier configured to identify the source system 40that generated/created the message, an entity identifier for the subjectentity (e.g., person actor, organization actor, location actor) of theevent and/or transaction corresponding to the message, a timestampindicating when the message was received and/or generated, and/or thelike. As will be recognized, there may be any discrete number ofentities that are identified as being associated with a message. And aspreviously noted, process 1200 of FIG. 12 could occur before, during, aspart of, and/or after the generation of the OPDO or the DAPDO.Similarly, process 1200 may be executed by the message pre-processingmodule 220 or the ingestion processing module 225.

At step/operation 820, once created, an OPDO or DAPDO can be provideddirectly to the ingestion processing module 225 for final processing andfiling based at least in part on various prioritization criteria. Inthis embodiment, the message OPDO or DAPDO is assigned to an electronicmessage processing queue for processing by the ingestion processingmodule 225. Alternatively, at step/operation 822, once created, a DAPDOcan be stored in a data store for deferred processing in a manner thatallows for extraction/retrieval of the same using entity identifiers.

Identity Matching Service

As previously referenced, as part of the process of generating a DAPDO(by the message pre-processing module 220 or the ingestion processingmodule 225), the ER system 100 generates an API request to an identitymatching service 240 to identify at least one entity (e.g., personactor, organization actor, location actor) associated with the message.After the identity matching service 240 determines an identity of theentity for the message, it provides an API response to the ER system 100to store the identity of the entity as part of the DAPDO.

The identity matching service 240 allows for the matching of any entity(e.g., person actors (patients, providers), organization actors(provider groups, insurance companies), location actors)) using matchingcriteria to determine whether the entities are already known to the ERsystem 100—e.g., associated with an existing ER or data object orwhether a new ER or data object should be generated/created for theentity. In one embodiment, the matching criteria evaluated are specificto the source system 40 (e.g., hospital laboratory system, payor claimadjudication system) that generated/created and provided the message.Any information/data in the ER system 100 can be used for matchingevaluation. Based at least in part on the information/data received by agiven interface, and the information/data existing in the ER system 100,the multi-variable, multi-criteria set of matching rules may yield amatch or no match. When a match is detected, the new information/dataevent can be used to persist the information/data into the ER system 100for the entity (e.g., person actor, organization actor, location actor)that is a subject of the event. For instance, the DAPDO is updated toinclude one or more entity identifiers configured to identify theentities. As will be recognized, in this context, entities may be personactors (patients, providers), organization actors (provider groups,insurance companies), and/or location actors. By identifying theappropriate entity or entities, the information/data can be stored inthe ER data store 211 corresponding to the same. If a match is notfound, then a new entity record is generated/created and theinformation/data event is attached to the newly generated/created dataobject (e.g., via a newly generated/created entity identifiercorresponding to the newly generated/created data object). Thereferences to the other persons indicated in the message (e.g.,physician, subscriber, guarantor, next of kin) and any organizationsindicated in the message are likewise evaluated using the matchingcriteria processed the same way—that is (1) if matched, the entityidentifier is returned for the existing entity (e.g., person actor,organization actor, location actor); if multiple matches, the entityidentifiers are returned for the existing entities (e.g., person actors,organization actors, location actors) for further evaluation; and (3) ifnot matched, a new entity identifier is generated/created for the entity(e.g., person actor, organization actor, location actor), along with anER and/or corresponding data object.

The identity matching service 240 allows for disparate information/dataabout an entity to be received from multiple sources and stored in anSBRO. The entities (e.g., person actors, organization actors, locationactors) indicated within a message are matched to the entities known tothe ER system 100 (or new data objects for the entities aregenerated/created if none previously exists). The identity matchingprocess 1200 returns an ontology concept identifier that has beenassigned to the entity (e.g., person actor, organization actor, locationactor) and referred to herein as an entity identifier. Alternatively,criteria matching may also be used, such as demographic information(e.g., name, birth date, gender, address, telephone number, emailaddress, mother's maiden name), identifiers (e.g., medical recordnumber, social security number, member number, provider ID, driver'slicense ID), relationship information/data (e.g., family data, serviceprovider relationship), and/or the like.

In one exemplary embodiment, all demographic source information/data istreated like any other data object; any demographic sourceinformation/data received in a message, form or web service input willgenerate/create an event. This permits the ER system 100 to display thedemographic date in the relevant event, and be able to “rematch” theentity (e.g., person actor, organization actor, location actor) and/orre-execute as needed. In another embodiment, when two entities (e.g.,person actors, organization actors, location actors) are recognized bythe ER system 100 as being the same entity, they can be merged (e.g.,via merge process), if necessary, by creating a third entity. Theinformation/data and events from the two entities are merged. Likewise,entities (e.g., person actors, organization actors, location actors) maybe unmerged (e.g., linked entities are separated). Audit histories maybe generated/created for each entity involved in the unmerging, andformerly merged entities can be accessed through the audit history. Inthe unmerging process, any events for the merged entity are assigned tothe appropriate lower level entity.

FIG. 12 provides a flowchart for exemplary process 1200 (e.g., identitymatching process 1200) illustrating various processes, procedures,and/or the like for identifying an entity via the identity matchingservice 240 (e.g., operating on the server 65). For example, in oneembodiment, the message pre-processing module 220 may be pre-processinga message to generate/create a DAPDO corresponding to the message andinvoke the identity matching service 240 (e.g., via an API call (e.g.,API request)). In another embodiment, the ingestion processing module225 may be processing an OPDO to generate a DAPDO and invoke theidentity matching service 240 (e.g., via an API call (e.g., APIrequest)). And in yet another embodiment, the extraction processingmodule 260 may be processing an EPDO to generate a DAPDO and invoke theidentity matching service 240 (e.g., via an API call (e.g., APIrequest)). The API request is a request for the identity matchingservice 240 to identify one or more entities (e.g., person actors,organization actors, location actors) indicated in the message. In anexample embodiment, one of the entities is the subject entity—the entitythat is the subject of the event, such as a patient—corresponding to themessage. In an example embodiment, the message may indicate otherentities (e.g., person actors, organization actors, location actors)that have various roles in the event corresponding to the message. Forinstance, the other entities may include providers, retailers,suppliers, physicians, lab technicians, nurses, locations, and/or thelike. As described above, a message is analyzed to identify and/ordetermine a plurality of attributes corresponding to one or moreentities. In various embodiments, each of the attributes may be a valueor a string that is associated with a class and/or ontology conceptidentifier. For example, the server 65 may determine a plurality ofattributes corresponding to the entity. For instance, a first attributeand a second attribute corresponding to the entity may be identifiedand/or determined from the message (e.g., via a reference from an OPDOor a DAPDO) and/or the like. For example, at least first and secondattributes are identified and/or determined by processing the messageusing an ontology concept identifier defined by a graph data structurecorresponding to the graph-based domain ontology. Such attributes mayinclude (1) an attribute type (e.g., Medicare health insurance claimnumber (HICN), Medicaid recipient ID, healthcare ID, national provideridentifier (NPI), medical record number), (2) attribute value (e.g., analphanumeric code providing a value corresponding to the attributetype), and (3) an assigning authority (e.g., the source assigningattribute values for the particular attribute type). The messagepre-processing module 220 may then generate/create and provide aninvoking API call (e.g., an API request) passing attributes (for eachattribute, the attribute type, value, and assigning authority (e.g., thesource assigning attribute values for the particular attribute type))corresponding to an entity referenced in a message to the identitymatching service 240. This API request invokes the identity matchingservice 240 to determine and/or identify an entity identifiercorresponding to an entity (if known) to the ER system 100 that is thesame entity referenced by the message.

At step/operation 1202, the identity matching service 240 is invoked byreceiving an invoking API call (e.g., an API request). For instance, theserver 65 (e.g., message pre-processing module 220, the ingestionprocessing module 225, and/or the extraction processing module 260) maygenerate/create and provide an invoking API call (e.g., an API request)invoking the identity matching service 240. In various embodiments, theinvoking API call (e.g., API request) provides the plurality ofattributes (for each attribute, the attribute type, value, and assigningauthority (e.g., the source assigning attribute values for theparticular attribute type)) corresponding to the entity that wereidentified and/or determined from the message. In various embodiments,assigning authority may be identified by a source identifier configuredto identify the source system 40 that generated/created the message. Invarious embodiments, the invoking API call (e.g., API request) may alsocomprise the entity role indicating the role of the entity in the eventcorresponding to the message to the identity matching service 240.

At step/operation 1204, a rule set is identified and accessed based atleast in part on the source identifier and/or entity role. For example,the server 65 may store and/or have access to a plurality of rule sets.The server 65 (e.g., via operation of the identity matching service 240)selects and/or identifies a rule set and accesses the rule set from theplurality of rule sets. In various embodiments, the rule set is selectedand/or identified from the plurality of rule sets based at least in parton the source identifier and/or entity role passed to the identitymatching service 240 (e.g., as part of the invoking API call (e.g., APIrequest) in an example embodiment). In various embodiments, each ruleset defines a sequence of attributes and/or attribute groups to be usedto identify the entity. For instance, an attribute group may comprisetwo or more attributes. For instance, the rule set may define a sequenceof attributes and/or attribute groups that are used, in sequence, toiteratively query the ER data store 211 to identify the entity. Forexample, the attributes may comprise entity names, entity identifiers,addresses, member identifiers, and/or the like. Similarly, theattributes or attribute groups may comprise (a) first names, last names,and middle initials; and (b) birthdates, genders, races, cities ofresidence, and/or the like. For instance, different source systems 40may include different information/data corresponding to an entity of aparticular entity role of an event. For example, different sourcesystems 40 may be known to provide various types of information/datawith more or less accuracy. In one embodiment, the rule set defines maybe used to determine (a) which of the first attribute and the secondattribute should be used to generate/create a first query used in afirst attempt to identify an entity known to the ER system 100 thatcorresponds to the first and second attributes, (b) which of the firstand second attributes should be used to generate/create a second queryused in a second attempt to identify an entity known to the ER system100 that corresponds to the first and second attributes, (c) thestructure and format of the queries, and/or the like. Using the rules,the various queries can be generated/created at once orgenerated/created in sequence based at least in part on whether or notthey will be used. That is, if the first query is successful, the secondquery would not need to be generated/created.

At step/operation 1206, the ER data store 211 is iteratively and/orsequentially queried based at least in part on at least a portion of thesequence of attributes and/or attribute groups. For example, theidentity matching service 240 (e.g., operating on the server 65) mayiteratively and/or sequentially query the ER data store 211 based atleast in part on at least a portion of the sequence of attributes and/orattribute groups. For instance, a first query may be generated/createdbased at least in part on the first attribute and/or attribute group ofthe sequence of attributes and/or attribute groups. The ER data store211 may be queried using the first query. In an example embodiment, theserver 65 determines that a match is made and/or an entity is identifiedbased at least in part on the first query when an entity that is knownto the ER system 100 (e.g., is associated with an entity identifier) isassociated with a corresponding attribute that matches the firstattribute or group of attributes.

In an example embodiment, if the first attribute is an attribute groupcomprising first name “Jane” and last name “Doe,” and an entity known tothe ER system 100 is associated with the first and last name attributes“Jane” and “Doe,” it is determined that a match is made and/or theentity is identified. In an example embodiment, if the first attributeis an attribute group comprising first name “Jane” and last name “Doe,”and an entity known to the ER system 100 is associated with the firstand last name attributes “Jan” and “Doe” or “J.” and “Doe,” it isdetermined that a match is not made and/or the entity is not identified.For example, in an example embodiment, the first attribute determinedfrom the processing of the message and the corresponding attribute ofthe entity known the ER system 100 must match 100% for the entity knownto the ER system 100 to be identified as being a match to the entitycorresponding to the first attribute determined from the processing ofthe message.

If the first query successfully identifies a match (e.g., identifies anentity corresponding to first attribute), the match (e.g., the entityidentifier for the identified entity known to the ER system 100) isreturned to the message pre-processing module 220, the ingestionprocessing module 225, and/or the extraction processing module 260(e.g., via an API response). If the first query does not successfullyidentify a match (e.g., does not identify an entity corresponding to thefirst attribute), a second query may be generated/created based at leastin part on the second attribute and/or attribute group of the sequenceof attributes and/or attribute groups. The ER data store 211 may bequeried using the second query. In an example embodiment, the server 65determines that a match is made and/or an entity is identified based atleast in part on the second query when an entity that is known to the ERsystem 100 (e.g., is associated with an entity identifier) is associatedwith a corresponding attribute that matches the second attribute orgroup of attributes, such as date of birth and gender and/or date ofbirth a known relative. If the second query successfully identifies amatch (e.g., identifies an entity corresponding to second attribute),the match (e.g., the entity identifier for the identified entity knownto the ER system 100) is returned to the message pre-processing module220, the ingestion processing module 225, and/or the extractionprocessing module 260 (e.g., via an API response). If the second querydoes not successfully identify a match, a third query may begenerated/created based at least in part on a third attribute and/orattribute group, as indicated by the selected rules set. This processcontinues until there is a match or until all queries have beenexhausted. And as previously noted, any iteration may also returnmultiple matches.

When it is determined (e.g., by the identity matching service 240operating on the server 65) that the queries defined by the rule sethave been exhausted without a match, it is determined that no match isfound, and the process continues to step/operation 1208. Atstep/operation 1208, it is determined (e.g., by the identity matchingservice 240 operating on the server 65) that the ER system 100 does notknow the entity referenced in the message, and in various embodiments,various actions may be taken. In an example embodiment, an error and/orexception is generated/created and provided to a user (e.g.,administrative user) via a user interface of a user computing entity 30and/or the like. In an example embodiment, a new entity/entity record isgenerated/created in the ER system 100, and a new entity identifier isgenerated/created and associated with the new entity/entity record. Thenew entity/entity record may be populated with the attributes determinedfrom the processing of the message that correspond to the entityreferenced in the message. The new entity identifier may then bereturned to the message pre-processing module 220 or the ingestionprocessing module 225 (e.g., via an API response at step/operation1210).

When it is determined (e.g., by the identity matching service 240operating on the server 65) that an entity (or entities) known to the ERsystem 100 and that corresponds to the entity referenced in the message,the process continues to step/operation 1210. At step/operation 1210,the entity identifier configured to identify the entity known to the ERsystem 100 and that corresponds to the known entity is provided and/orpassed to the message pre-processing module 220, the ingestionprocessing module 225, and/or the extraction processing module 260. Inan example embodiment, the entity identifier configured to identify theentity known to the ER system 100 and that corresponds to the entityreferenced in the message is provided and/or passed to the messagepre-processing module 220 or the ingestion processing module 225 via anAPI response. For instance, the message pre-processing module 220 or theingestion processing module 225 may receive the API response, and basedthereon and/or responsive thereto, update or modify the DAPDO for thecorresponding message to include the identified entity identifier inassociation with the corresponding user role. For example, receiving theAPI response may cause the message pre-processing module 220 to update,modify, and/or generate/create the DAPDO corresponding to the message toinclude the entity identifier in association with the corresponding userrole.

As described herein, the message pre-processing module 220, theingestion processing module 225, and/or the extraction processing module260 can invoke the identity matching service 240 and receive a responsefrom the identity matching service 240. For example, in someembodiments, the message pre-processing module 220, the ingestionprocessing module 225, and/or the extraction processing module 260 maypass an invoking API call (e.g., an API request and/or otherwise invoke)to the identity matching service 240 and receive a corresponding APIresponse from the identity matching service 240.

As noted previously, OPDOs and DAPDOs can be stored in processing queuesand/or passed to the ingestion processing module 225 for prioritizedprocessing, at step/operation 820. Similarly, DAPDOs can be stored fordeferred processing (e.g., user interaction event processing), atstep/operation 822. In deferred processing (e.g., user interaction eventprocessing), the DAPDO is stored in a data store in manner that allowsfor extraction/retrieval of the same using entity identifiers.

Various embodiments provide technical solutions to the technical problemof identifying an entity known to the ER system 100 based at least inpart on information/data corresponding to the entity provided by asource system 40 (e.g., as part of message). For instance, by using arules set that is particular to the source system 40 that provided themessage, attributes corresponding to the entity extracted from themessage may be used to query the ER data store 211 in an efficientmanner such that the attributes most likely to provide a dependablematch (based at least in part on the past behavior of the source system40) are used before attributes that are less likely to provide adependable match (based at least in part on the past behavior of thesource system 40). Embodiments of the present invention therefore reducethe computational resources required to identify an entity known to theER system 100 based at least in part on information/data correspondingto the entity provided by a source system 40. This allows the ER system100 to allocate processing resources, memory resources, and/or othercomputational resources to other tasks while executing the identitymatching services in parallel. Thus, various embodiments of the presentinvention therefore provide improvements to the technical field ofidentity matching services.

Deferred Processing of Messages

As noted previously, in prioritized processing, OPDOs and DAPDOs can beassigned to an electronic message processing queues for processing bythe ingestion processing module 225. This allows for OPDOs and DAPDOs tobe ingested by the ingestion processing module 225 in accordance withthe prioritization rules and definitions of the corresponding electronicmessage processing queue.

In deferred processing (e.g., user interaction event processing), DAPDOscan be stored in a data store in manner that allows forextraction/retrieval of the same using entity identifiers (for thecorresponding person actors, organization actors, location actors). Inthis embodiment, at a later point in time, a trigger corresponding to astored DAPDOs may be identified, and the DAPDOs may be processedresponsive thereto to update one or more ERs stored in the ER data store211. In various embodiments, the trigger corresponding to a stored DAPDOis a trigger to process any DAPDOs corresponding to a subject entity orother entity having a relationship with the subject entity identified bythe trigger, for instance. In one embodiment, the trigger may be a userinteraction event, such as a member/patient logging into a user portal2610 (see FIG. 26). For example, the member/patient (e.g., subjectentity) may log into a user portal 2610 to view information/datacorresponding to the user's (e.g., subject entity's) ER stored in the ERdata store 211. In this example, a query for all DAPDOs associated withthe member/patient (e.g., comprising the corresponding entityidentifier) is generated and the corresponding DAPDOs are returned forprocessing by the ingestion processing module 225. In another example,the user interaction event may be that of a provider or other entity. Inthis example, a query for all DAPDOs for the provider or formembers/patients of the provider (e.g., using any necessary entityidentifiers) is generated, and the corresponding DAPDOs are returned forprocessing by the ingestion processing module 225. For instance, aphysician or staff member at the practice where the physician practicesand that has an active attending physician relationship, for instance,with the subject entity may log in to a portal 2610. The logging in tothe portal by the physician or staff member may cause a triggercorresponding to subject entities to be identified based at least inpart on corresponding entity identifiers (for the corresponding personactors, organization actors, location actors). For example, when thephysician or staff member logs into the portal 2610 via a user profile(comprising an entity identifier), each subject entity having an activerelationship with the physician of one or more relationship types isidentified. In response, one or more queries for any DAPDOs is generatedand the corresponding DAPDOs are returned for processing by theingestion processing module 225.

In other embodiments, triggers may be identified based at least in parton predicted or anticipated user interaction events. For instance, ascavenger process may operate to identify subject entities for which thestored DAPDOs exist and trigger the processing of those stored DAPDOs ina prioritized manner, based at least in part on past user interactionevents using one or more machine learning models. For example, if asubject entity generally logs into a portal 2610 on Wednesday morning,the one or more machine learning models may identify this pattern andpredict that the subject entity will access the ER system 100 onWednesday morning and consequently trigger the processing of thecorresponding DAPDOs on Tuesday night. In another example, the scavengerprocess may be configured to select subject entities having storedDAPDOs based at least in part on a user priority paradigm. In an exampleembodiment, the user priority paradigm takes into account (a) if asubject entity has ever logged in to a portal 2610, (b) if an individualthat has access to the subject entities ER (e.g., physician or otherentity who a relationship that would permit them with access to thesubject entity's ER) has ever logged into a portal 2610, (c) howfrequently the subject entity logs in to the portal 2610, (d) howfrequently an individual that has access to the subject entity's ER logsin to the portal 2610, and/or the like. Once all of the stored DAPDOscorresponding to higher priority subject entities are processed, thestored DAPDOs corresponding to lower priority subject entities areprocessed (possibly in a random order). In various embodiments, avariety of priority schemes may be used to cause triggers correspondingto one or more subject entities to be identified and thereby causingDAPDOs corresponding to the one or more subject entities to be processedby the ingestion processing module 225.

In an example embodiment, this form of deferred data ingestionprocessing and/or prioritized data ingestion processing (e.g., messageprocessing) may be an alternate form of data ingestion. For instance, aprimary form of data ingestion for the ER system 100 may include thecomplete processing of each message as the message is received via thestream of messages. For example, when a message is received, the messagemay be pre-processed by the message pre-processing module 220 togenerate/create DAPDOs for provision to the ingestion processing module225 for processing (and/or queuing for processing) in real-time or nearreal-time with respect to the generation of the DAPDO. However, when theincoming message volume is too high and/or the availability systemresources are too low (insufficient memory resources, processingresources, network resources, and/or the like), the form of dataingestion used by the ER system 100 may be dynamically changed from theprimary form of data ingestion (e.g., real-time or near real-time dataingestion) to the alternate deferred data ingestion processing. Thus,when configurable resource thresholds are satisfied, the deferred dataingestion processing may be dynamically initiated. Further, when theconfigurable resource thresholds are satisfied (e.g., the incomingmessage volume is sufficiently low and/or the availability systemresources are sufficiently available), the primary form of dataingestion may again be initiated.

FIG. 15 provides a flowchart for exemplary process 1500 illustratingvarious processes, procedures, steps, operations, and/or the like forperforming deferred processing (e.g., user interaction eventprocessing), according to an example embodiment. Starting atstep/operation 1502, a batch or stream of messages are received via oneor more communications interfaces. For instance, a server 65 (e.g., viacommunications interface 208) may receive a batch or stream of messages.In various embodiments, each message may correspond to an event and/ortransaction. For example, a plurality of source systems 40 maygenerate/create messages and provide the messages such that the server65 receives the messages as a stream of messages.

At step/operation 1504, each message in the batch or stream ispre-processed in accordance with process 800. For instance, each messagereceived by the server 65 may be passed to the message pre-processingmodule 220. The message pre-processing module 220 may then generate aDAPDO for each message. At step/operation 1506, DAPDOs can be stored fordeferred processing (e.g., user interaction event processing). Forinstance, the server 65 may store the DAPDOs in non-volatile memory 206and/or volatile memory 207 for deferred processing (e.g., userinteraction event processing).

At step/operation 1508, a triggering event corresponding to at least onesubject entity identifier is automatically detected, identified, and/orthe like. For instance, the processing element 205 of the server 65 mayautomatically detect and/or identify a triggering event corresponding toat least one subject entity identifier. For example, as described above,the triggering event may correspond to a user interaction event—a user(e.g., associated with an entity identifier) logging into a user portal2610. The user logging in may be a (a) patient/member, (b) physician,provider, or provider staff member having relationships with one or morepatients/members or organizations, and/or the like. Similarly, thetriggering event may be a scavenger process and/or a prioritizedscavenger process selecting the subject entity, and/or the like. In anexample embodiment, a triggering event may be associated with one ormore subject entity identifiers (e.g., via a user profile). Forinstance, the triggering event may comprise various information/datacorresponding to the subject entity and/or another entity having arelationship with the subject entity (e.g., a physician or otherprovider or provider staff) and the triggering event may be processed toidentify one or more subject entities for which messages should beprocessed. For instance, the triggering event may be processed to causethe identity matching service 240 to be invoked to determine one or morecorresponding entity identifiers (for the corresponding person actors,organization actors, location actors). In an example embodiment, thedetection of a triggering event may include invoking a dataaccess/function controller 255 to determine which subject entities havea relationship with a physician, provider, or provider staff member thatallows the same to access one or more parts of the ERs corresponding tothe subject entities. In the provider example, one or more queries forall DAPDOs for the provider or for members/patients of the provider(e.g., using any necessary entity identifiers) is generated (e.g., via agetMessageDAPDO( ) method) and the corresponding DAPDOs are returned forprocessing by the ingestion processing module 225. Similarly, in thepatient/member context, a query for all DAPDOs for the member/patient isgenerated and the corresponding DAPDOs are returned for processing bythe ingestion processing module 225.

At step/operation 1510, the DAPDOs corresponding to the subject entitiesare provided for processing to the ingestion processing module. Forexample, the server 65 may execute the ingestion processing module 225to complete the processing of the DAPDOs as described with regard toprocess 1300.

At step/operation 1512, after any returned DAPDOs are provided forprocessing to the ingestion processing module 225 for processing, theserver 65 can identify active relationships or related entitiesassociated with the user (e.g., relationships in which a member/patientuser has access to a spouse's ER or a child's ER)—e.g., via agetAccessibleEntities( ) method. At step/operation 1514, for activerelationships or related entities associated with the user, the server65 can generate one or more queries for any DAPDOs (using the respectiveentity identifiers) associated with the entities identified by theactive (direct or indirect) relationships (e.g., via agetMessageDAPDOOfAccessibleEntities( ) method), such as described withregard to process 2900. And in response, any corresponding DAPDOs arereturned for processing by the ingestion processing module 225. Thus,this process provides for deferred processing of DAPDOs directlyassociated with a user (e.g., patient/member, physician, provider,caregiver) and DAPDOs indirectly associated with a user.

As will be recognized, various embodiments provide technical solutionsto the technical problem of efficiently submitting DAPDOs to theingestion processing module 225 based at least in part on systemresource availability and allocation. In particular, the deferredprocessing of DAPDOs to the ingestion processing module 225 in thedescribed prioritized manner is transparent to the user and allows theER system 100 to dynamically allocate resources based at least in parton need and availability, instead of creating a processing bottleneck.This approach also ensures that a user's information/data is the mostup-to-date available.

Ingestion Processing

In various embodiments, the ingestion processing module 225 isconfigured to receive OPDOs or DAPDOs, process the same togenerate/create one or more data transfer objects, and provide the oneor more data transfer objects that may be then be used togenerate/create, update, and/or manage one or more ERs stored in the ERdata store 211. In various embodiments, the ingestion processing module225 generates a container tree data structure based at least in part oninformation/data of each DAPDO. FIG. 10B represents an exemplarycontainer tree data structure. The ingestion processing module 225 maythen traverse the container tree data structure in a depth-firsttraversal (e.g., working from the leaf nodes to the root node) togenerate/create the data transfer objects to generate/create, update,and/or manage one or more ERs.

FIG. 13 provides a flowchart for exemplary process 1300 (e.g., ingestionprocess 1300) illustrating various processes, procedures, steps,operations, and/or the like performed by an ingestion processing module225 operating on and/or being executed by a server 65 to process an OPDOor a DAPDO to generate/create data transfer objects that may be appliedto an ER stored in the ER data store 211 to update and/or modify one ormore data objects (e.g., SBROs) of the ER. Starting at step/operation1302, the ingestion processing module 225 receives an OPDO or a DAPDO.For example, the message pre-processing module 220 may generate/createan OPDO or a DAPDO by performing pre-processing of a messagegenerated/created and provided by a source system 40. The messagepre-processing module 220 may then provide the OPDO or the DAPDO suchthat the ingestion processing module 225 receives the same.

At step/operation 1304, the ingestion processing module 225 reads theinformation from the OPDO or DAPDO.

If an OPDO is received, at step/operation 1306, the ingestion processingmodule generates/creates a DAPDO based at least in part on the OPDO, asdescribed with regard to steps/operations 812, 814, 816, and 818 of FIG.8. For example, the OPDO (along with other information/data) istransformed from an XML document used for storage and transmission to aJava object that is executable or used for execution, such as viaunmarshalling. Thus, the DAPDO comprises the structure of the OPDO withadditional information/data that allows for the construction of acontainer tree data structure to ingest information/data identified inthe OPDO. For example, each DAPDO comprises a DAPDO identifier,information/data from the corresponding OPDO, a DAPDO type (indicate theinformation/data it contains), an owner, a reference (e.g., a GUID) tothe corresponding message, and a reference to the source system. Thisallows the DAPDO to be generated/created with the necessaryinformation/data. Additionally, this may include invoking the identitymatching service 240 and its corresponding steps/operations to identifythe message owner and any other entities referenced by the message. Thesteps/operations of these processes are not repeated here, but areincorporated by reference. If a DAPDO is received, step/operation 1306is bypassed.

In that regard, either after reading the DAPDO and/orgenerating/creating a DAPDO, the ingestion processing module has theinformation/data to generate/create a tree data structure based at leastin part on the DAPDO. The information/data may comprise one or moreobservation groups and a message identifier configured to identify theraw message. In various embodiments, the information/data in the DAPDOmay also comprise entity identifiers (for the corresponding personactors, organization actors, location actors) associated with roleidentifiers; timestamps corresponding to when the measurements and/ordeterminations resulting in the values provided by the observations ofthe observation group were taken; a status corresponding to a treatment,lab work, test, and/or the like referenced in the DAPDO (e.g., whetherthe test has been ordered, test results have been returned, a claimcorresponding to the test has been processed, and/or the like); a sourceidentifier identifying the source system 40 that generate/create themessage; and/or the like. In various embodiments, the observationgroup(s) were previously extracted from the OPDO provided as part of theDAPDO. In an example embodiment, a data object is generated/created foreach observation group. In an example embodiment, a data object maycomprise a plurality (e.g., two or more) observation groups.

At step/operation 1308, the ingestion process 1300 constructs acontainer tree data structure based at least in part on the DAPDO. Invarious embodiments, a data artifact packet container node isgenerated/created. The data artifact packet container node is thereceptacle that holds all other container nodes of the container treedata structure and is the root node of the container tree datastructure. See FIG. 10B. The graph-based domain ontology definesontology concepts with appropriate selectable observables togenerate/create the container tree data structure and to cause thecontainer tree data structure to be populated. For instance, containernodes of the container tree data structure are populated with theconcept identifiers and their observation values provided by theobservations of the observation group(s) of the DAPDO. Each of thecontainer nodes in the container tree data structure contains conceptidentifiers and their observation values used to populate data transferobjects. Container nodes are recursive but dependent upon the graph datastructure representing the graph-based ontology. For example, thecontainer tree data structure comprises a plurality of nested containernodes held within the root node (e.g., the data artifact packetcontainer node). The leaves of the container tree data structurecomprise concept identifiers and their observation values correspondingto the graph-based ontology concept of the parent container containingthe leaf node. For instance, the observables in an observable group arerelated to one another in a manner described in the graph data structurerepresenting the graph-based ontology. This graph data structure defineswhich container nodes can be subcontainer nodes of other containernodes. For example, an entity referenced in the DAPDO corresponds to anentity container node in the container tree data structure and theentity's name(s) corresponds to a subcontainer node of the entitycontainer node (e.g., an entity name container). However, the reverse isnot true. In particular, an entity name container cannot be asubcontainer node of an entity container node because this structure isnot supported by the graph-based ontology or the graph data structurerepresenting the graph-based ontology.

As noted above, at step/operation 1308, a container tree data structureis constructed based at least in part on the DAPDO. FIG. 14 providesadditional detail of process 1308 (e.g., the container tree datastructure construction process 1308) illustrating various processes,procedures, steps, operations, and/or the like performed (e.g., by theingestion processing module 225 executed by the processing element 205of the server 65) to construct the container tree data structure, in anexample embodiment. Starting at step/operation 1402, an observation isread from the DAPDO. For instance, the ingestion processing module 225may read an observation from the DAPDO. As described above, anobservation comprises an ontology concept identifier and a correspondingvalue.

At step/operation 1404, based at least in part on the graph-basedontology, the type of container which should contain the observable isdetermined. For example, the ingestion processing module 225 (beingexecuted by the processing element 205 of the server 65) may determinewhich type of container should contain the observable based at least inpart on the graph-based ontology. Some non-limiting examples ofcontainer types include an entity container node; entity name containernode which may have subcontainer nodes discrete entity name containerand/or composite entity name container; address container which may havesubcontainer nodes discrete address container and composite addresscontainer; amount container; blob container; certification container;contract service plan container; contract services container; datacorrection container; data correction pairs container; email container;employment container; item (e.g., health item) container; health statusevaluation container node; health task container node; identifiercontainer node; language container node; language proficiency containernode; location container node; message container node; note containernode; observation element container node; occupation container node;plan descriptor container node; relationship container node; role fieldcontainer node; specialty container node; specimen container node;system task container node; system task schedule container which mayhave subcontainer nodes interval task schedule container and/orsingleton task schedule container node; telephone container which mayhave subcontainer nodes discrete telephone container and/or compositetelephone container node; temporal container which may have subcontainernodes ISO temporal container node; HL7 temporal container node;specified format temporal container node; test container node; valuecontainer node; website container node; and/or the like. Variousembodiments may use a variety of types of container nodes appropriatefor the domain being considered.

In various embodiments, an ontology concept is associated with aparticular type of container. For instance, an entity identifier maycorrespond to container having the type entity container node. Inanother example, ontology concepts corresponding to items (e.g., healthitems) may correspond to container nodes having a type heath itemcontainer. Thus, the ingestion processing module 225 may determine whichtype of container corresponds to the read observation. To do so, theingestion processing module 225 may query the graph-based ontology toidentify the container type that should contain the corresponding items(e.g., observables).

At step/operation 1406, it is determined if a container node of the typeof container determined to correspond to the read observation is presentin the container tree data structure. For example, the ingestionprocessing module 225 may determine whether a container node of the typeof container determined to correspond to the read observation is presentin the container tree data structure. For instance, the ingestionprocessing module 225 may review and/or analyze the container tree datastructure to determine whether a container node of the type of containerdetermined to correspond to the read observation is present in thecontainer tree data structure.

If, at step/operation 1406, it is determined that a container node ofthe type of container corresponding to the read observation is presentin the container tree data structure, the process continues tostep/operation 1408. At step/operation 1408, the observation is added tothe container node of the type of container corresponding to the readobservation. For example, the ingestion processing module 225 may addthe read observation to the container within the container tree datastructure that has the type determined to correspond to the readobservation.

If, at step/operation 1408, it is determined that a container node ofthe type of container corresponding to the observation is not present inthe container tree data structure, the process continues tostep/operation 1410. At step/operation 1410, a new container node of thetype of container corresponding to the read observation is constructed.For instance, the ingestion processing module 225 may construct, build,generate, and/or similar words used herein interchangeably a newcontainer node that has the type that corresponds to the readobservation. At step/operation 1412, the read observation is added tothe new container node. For example, the ingestion processing module 225may add the read observation to the new container node that has the typedetermined to correspond to the read observation. At step/operation1414, the new container node is inserted into the appropriate locationin the container tree data structure. For instance, the ingestionprocessing module 225 may determine the appropriate location for the newcontainer node within the container tree data structure and insert thenew container node into the container tree data structure at thedetermined appropriate location. For example, the container node may bea type that is a subcontainer node of another container (e.g., adiscrete entity name container may be a subcontainer node of an entityname container but not a sub container node of a composite entity namecontainer). In various embodiments, the appropriate location of the newcontainer node is determined based at least in part on the graph-basedontology. In various embodiments, step/operation 1414 may be performedprior to step/operation 1412.

Once each of the observations of the DAPDO has been read and added tothe container tree data structure, the ingestion processing module 225may proceed to assembling (performing an assembly process via an invokedassembly method) the container tree data structure to generate/createthe data transfer objects. In other words, once the container tree datastructure has been constructed and populated based at least in part onthe observation group(s) of the DAPDO, the container nodes of thecontainer tree data structure are assembled, at step/operation 1310. Theassembly process may generate/create one or more data transfer objectsbased at least in part on the container tree data structure. Forinstance, each container node may be assembled and return an assembleddata transfer object as its return value. To assemble, each observationin the corresponding container node is individually, and without respectto order, evaluated to identify the data its value attribute holds. Onceidentified individually, the value of the container node can beassembled and evaluated for minimum requirements in order togenerate/create a viable data transfer object. For example, it may bedetermined whether the value of the container is a reasonable and/ormeaningful value for the container. For instance, if the container is anentity first name container node, the value of the container is expectedto be a string of letters. If the value of the entity first namecontainer is a string including numbers, the value of the container nodedoes not satisfy the minimum requirements for creating a viable datatransfer object, in an example embodiment. In another example, if thecontainer node is a blood pressure container node, the container isexpected to have a diastolic blood pressure value in the range of 0 to110, for instance, and a systolic blood pressure value in the range of 0to 200, for example. If the value of the blood pressure container nodecomprises one or more numbers that are outside of the correspondingrange, the container node does not satisfy the minimum requirement forcreating a viable data transfer object, in an example embodiment.

In various embodiments, an ontology concept has three persistableattributes: the source vocabulary, source vocabulary code, and anoptional text description. In order to cull the desired information fromthe message and the three attributes of source vocabulary, sourcevocabulary code, and optional text description, three separateobservations (key-value pairs consisting of a concept identifier (e.g.,observable identifier) and a corresponding value) are resolved to asingle ontology concept. For example, a triplet of observations may bedefined based at least in part on the corresponding source vocabulary,source vocabulary code, and the optional text description provided bythe message. For instance, during the assembly process, aggregators mayresolve the triplet of observations into a single ontology concept.

Additionally, a message sometimes contains a primary and alternate setof source vocabulary or vocabulary codes that represent the sameinformation/data. The graph-based ontology concepts resolved for theprimary set and the alternate set of source vocabulary or vocabularycodes that represent the same data may be compared (e.g., by theaggregators of the ingestion processing module 225) to select the morespecific ontology concept. For example, the primary set of vocabulary orvocabulary codes may be resolved to the graph-based ontology concept ofbroken left arm and the alternate set of vocabulary or vocabulary codesmay be resolved to the graph-based ontology concept of broken lefthumerus. The graph-based ontology concept of broken left humerus isselected as the more specific ontology concept and used in furtherprocessing.

In various embodiments, to prevent recursions and attempts to assemblecontainer nodes with errors, during assembly, a container node exists inone of four states: not assembled (assembly of the container node hasnot yet been attempted), undergoing assembly (the container node iscurrently being assembled), assembled (the container node has beensuccessfully assembled), and unable to assemble (the attempt to assemblethe container node has resulted in an exception). For instance, if,during assembly, it is determined that the value of a container nodedoes not meet the minimum requirements for creating a viable datatransfer object (e.g., a blood pressure value is negative, an entityfirst name is a string of numbers, and/or the like), the assembly of thecontainer node may be halted and the state of the container node may beset to unable to assemble. Any exceptions or warnings encountered duringthis process are added to the appropriate collections (e.g., a warningscollection, an exceptions collection, and/or the like). For example,when a container is assigned the state unable to assemble, an exceptionor warning may be logged such that the container and/or the value of thecontainer node may be manually reviewed. In various embodiments,exceptions are used to associate a Java exception with an ontologyconcept. This allows further information/data such as expandeddescriptions and suggested resolutions.

In various embodiments, the ingestion processing module 225 is executed(e.g., by the processing element 205 of the server 65) to assemble oneor more data transfer objects based at least in part on the containertree data structure by traversing the container tree data structure in adepth-first traversal. For instance, the container tree data structuremay be traversed starting at a leaf node (e.g., a value of a containernode) upward (e.g., via a depth-first traversal) to the root node of thecontainer tree data structure. In the healthcare context, each datatransfer object corresponds to a particular health item. For example,each data transfer object corresponds to one or more elements of anevent and/or transaction. For instance, a data transfer objectcorresponding to blood pressure may be generated/created. The datatransfer object comprises an entity identifier that may be used toidentify the ER corresponding to the subject entity (e.g., the personwhose blood pressure measurement is provided by the data transferobject), an ontology concept identifier identifying the health item towhich the data transfer object corresponds (e.g., blood pressure), pairsof ontology concept identifiers and corresponding values indicating theobservations (e.g., an ontology concept identifier for diastolic bloodpressure and the measure diastolic blood pressure value, an ontologyconcept identifier for systolic blood pressure and the measured systolicblood pressure value), a timestamp indicating when the blood pressuremeasurement was taken, one or more provider entity identifiers (e.g.,identifying the hospital/clinic where the measurement was taken, thephysician and/or physician practice where the measurement was taken, thenurse/technician who took the measurement, and/or the like), and/or thelike. For example, the data transfer object may compriseinformation/data that may be used to update an ER record, perform anSBRO process 1600, determine a confidence score for an SBRO element,and/or the like.

After completing the assembly process (e.g., once all the containernodes have a status of one of assembled and/or unable to assemble)and/or responsive to completing the assembly process, the ingestionprocessing module 225 is executed (e.g., by the processing element 205)to provide the one or more data transfer objects generated/created byassembling the container tree data structure to an SBRO process 1600, atstep/operation 1312. For instance, the ingestion processing module 225may provide the one or more generated/created data transfer objects foruse in performing a database update function. In various embodiments,the database update function may be used to modify one or more ERsand/or SBROs of an ER stored in the ER data store 211 and/or perform adatabase insert function (e.g., insert one or more new ERs and/or insertone or more new SBROs of an ER). For example, the ingestion processingmodule 225 may pass the one or more generated/created data transferobjects to the SBRO processing module 265. The SBRO processing module265 may then be executed to apply the data transfer objects tocorresponding SBRO of the subject entity's ER. In various embodiments, adata transfer object may be used to update SBROs that are not part ofthe subject entity's ER. For instance, a data transfer object may beused to update an SBRO corresponding to a provider entity (e.g., anattending physician). In an example embodiment, a data transfer objectmay be used to update a relationship SBRO (e.g., that indicates arelationship between the subject entity and the provider entity, forinstance). The SBRO process 1600 is described in more detail elsewhereherein.

The ingestion processing module 225 provides technical solutions to thetechnical problem of how to efficiently and effectively generate/createdata transfer objects for use in the automated managing, ingesting,monitoring, updating, and/or extracting/retrieving of information/dataof a 211 data store. In particular, the generation of the container treedata structure and the assembling of the container tree data structureto generate/create the data transfer objects provides a streamlinedprovides for an efficient method of generating data transfer objectsthat are applied to elements of an ER (e.g., via an SBRO process). Thisprovides for a simplified approach for processing complexinformation/data. Additionally, the generation of the container treedata structure and the assembling of the container tree data structureto generate/create the data transfer objects takes advantage of theimprovements provided through the use of the graph-based ontology.Additionally, after ingestion, each DAPDO is stored in a data storearchive that allows for a complete audit history of all changes toinformation information/data in the ER system 211. In other words, bypersisting a copy of each DAPDO, the source of any change can be tracedto a corresponding DAPDO and consequently message and source system.Thus, various embodiments provide technical improvements in the field ofdata ingestion and processing, for example, for the automated managingand updating of a 211 data store.

SBRO Processing

As described above, the ingestion processing module 225 assembles a dataartifact packet tree data structure to generate/create one or more datatransfer objects. Once the data artifact packet tree data structure isassembled, the single best record object (SBRO) process 1600 isinitiated.

In various embodiments, the ER data store 211 may comprise a pluralityof ERs corresponding to subject entities. In various embodiments, eachER comprises a plurality of SBROs. In one embodiment, each ontologyconcept (as identified by its ontology concept identifier) may beassociated with an SBRO or a plurality of SBROs. The SBRO comprises adata object comprising the “best” information/data known about thegraph-based ontology concept corresponding to a subject entity (or pairof entities in the case of a relationship SBRO). For instance, amammogram might first be reported via an appointment request, thensubsequently by a visit summary when the patient checks in, then areport of the exam several days later, followed by a claim submissionfor payment, and finally the payment for the exam. SBRO allows each ofthese events to refer to the same event using an SBRO. For an entity(also referred to herein as an actor, subject entity, and/or the like)various types of information/data can be processed through the SBROprocess 1600. For example, items (e.g., health items), relationships,observations, and/or the like can all be processed as described herein.

To account for the multiple messages and/or pieces of information/data,the SBRO process 1600 involves at least two steps/operations: (1)determining the appropriate ontology concept for the information/data inthe SBRO process 1600 (using the programmatic reasoning logic 230) and(2) determining the fitness of the information/data. Once theappropriate ontology concept for the information/data of a data transferobject is determined (e.g., using the programmatic reasoning logic 230),the SBRO process 1600 evaluates the information/data from the datatransfer object with that of the stored data object for the sameontology concept (based at least in part on the concept identifier). TheSBRO process 1600 then evaluates the information/data from the datatransfer object with that of the stored data object for semanticresolution, temporal resolution, and resolution of provenance andreliability (e.g., possibly based at least in part on the source system40 that generated/created the message resulting in the data transferobject and/or information/data stored in the SBRO). With regard tosemantic resolution, the SBRO processing module 265 determines whetherthe information/data elements are disparate events or a related event.For temporal resolution, the SBRO processing module 265 evaluateswhether the sequence of events and the dates and times reported areconsistent with a single event or multiple occurrences of the same typeof event. For instance, the SBRO processing module 265 can determinewhether two information/data elements relate to a single persistentchest infection or two distinct cases of chest infection in the sameindividual. And for the resolution of provenance and reliability, theSBRO processing module 265 evaluates the reliability of theinformation/data elements (e.g., based at least in part on thecorresponding source system 40). For example, an indirect patient reportof a laboratory result has a different level of imputed reliability thanthat same information/data directly reported by the laboratory system.The SBRO processing module 265 transforms partial, overlapping,elementary information/data transactions into a coherent ER of anindividual's healthcare events and state of health. For instance, asingle healthcare event, such as a procedure can easily give rise tothirty or more information/data transactions spread across varioussource systems. However, the SBRO processing module 265 enables thosethirty or more transactions to be represented via a single SBRO of thesubject entity's ER.

FIG. 16 provides a flowchart for exemplary process 1600 (e.g., SBROprocess 1600) illustrating various processes, procedures, steps,operations, and/or the like performed, for example, by an SBROprocessing module 265 executing on a processing element 205 of a server65, to determine which information/data to include in an SBRO and toupdate the SBRO appropriately. Starting at step/operation 1602, a datatransfer object is received. For instance, the ingestion processingmodule 225 may generate/create a data transfer object and pass the datatransfer object to the SBRO processing module 265. The SBRO processingmodule 265 may then receive the data transfer object.

At step/operation 1604, the SBRO type corresponding to the data transferobject is determined based at least in part on the graph-based ontology.For example, the server 65 may determine the SBRO type corresponding tothe data transfer object. For instance, the SBRO processing module 265may invoke programmatic reasoning logic 230 and/or otherwise causeprogrammatic reasoning logic 230 to determine an SBRO type correspondingto the data transfer object. For example, the programmatic reasoninglogic 230 (e.g., via processes 1700A and/or 1700B) may analyze ontologyconcept identifiers present in the data transfer object to identifyand/or determine an SBRO type corresponding to the data transfer object.In various embodiments, an SBRO may correspond to an item (e.g., ahealth item), a relationship (e.g., between two entities), subjectentity identifying information/data (e.g., name, birthdate, socialsecurity number, insurance member identification number, and/or thelike), subject entity contact information/data (e.g., address, telephonenumber, email, and/or the like), and/or other ontology concepts and theSBRO type indicates the ontological concept corresponding to the SBRO.For instance, the data transfer object may correspond to the ontologicalconcept of a fractured left humerus. The SBRO processing module 265 maydetermine, based at least in part on the ontological conceptcorresponding to the data transfer object that the SBRO typecorresponding to the data transfer object is a broken arm SBRO type.

At step/operation 1606, it is determined whether an SBRO of thedetermined SBRO type (e.g., the SBRO type determined to correspond tothe data transfer object) exists in the ER corresponding to the subjectentity. For example, the SBRO processing module 265 may invoke the DSML245 (e.g., via one or more API calls (e.g., API requests), in an exampleembodiment) to access at least a portion of ER corresponding to thesubject entity. In an example embodiment, the SBRO processing module 265may request that the DSML 245 return any SBRO of the ER corresponding tothe subject entity and having the determined SBRO type (e.g., the SBROtype determined to correspond to the data transfer object).

When the DSML 245 returns a null response, it is determined that an SBROof the determined SBRO type does not exist in the ER corresponding tothe subject entity and the process continues to step/operation 1608.Similarly, when the DSML 245 returns a response comprising one or moreSBROs of the determined SBRO type from the ER corresponding to thesubject entity, the DSML 245 loads the SBROs into cache memory from theER data store 211. The SBRO processing module 265, via use of theprogrammatic reasoning logic 230 as described herein, may determinewhether any of the SBROs of the determined SBRO type and read/loadedfrom the ER corresponding to the subject entity correspond to the samehealth item, event, and/or transaction as the data transfer object. Forinstance, if the data transfer object is associated with the ontologicalconcept fractured left humerus and the determined SBRO type is brokenarm, an SBRO may be returned that corresponds to a fractured right ulna,it is determined that existing SBRO read/loaded from the ERcorresponding to the subject entity does not correspond to the samehealth item, event, and/or transaction as the data transfer object. Inanother example, the SBRO returned from the ER corresponding to thesubject entity may correspond to a fractured left humerus that wasdiagnosed six years ago, and it may be determined that the existing SBROread/loaded from the ER corresponding to the subject entity does notcorrespond to the same health item, event, and/or transaction as thedata transfer object. In yet another example, the SBRO returned from theER corresponding to the subject entity may correspond to a fracturedleft humerus that was diagnosed one month ago and it may be determinedthat the existing SBRO read/loaded from the ER corresponding to thesubject entity does correspond to the same health item, event, and/ortransaction as the data transfer object. When it is determined that theSBRO of the determined SBRO type corresponds to the same health item,event, and/or transaction as the data transfer object, the processcontinues to step/operation 1610. In various embodiments, programmaticreasoning logic 230 may be used to perform steps/operations 1604 and/or1606. For example, the SBRO processing module 265 may invokeprogrammatic reasoning logic 230 (e.g., via one or more API calls (e.g.,API requests), in an example embodiment) to complete various portions ofsteps/operations 1604 and/or 1606.

At step/operation 1608, since there is not an existing SBROcorresponding to the same health item, event, and/or transaction as thedata transfer object in the ER corresponding to the subject entity, anew SBRO corresponding to the health item, event, and/or transactioncorresponding to the data transfer object and corresponding to thesubject entity is generated/created. For instance, the SBRO processingmodule 265 may generate/create a new SBRO of the determined SBRO type(e.g., the SBRO type determined to correspond to the data transferobject) and populate the new SBRO based at least in part on the datatransfer object. For example, a new SBRO may be initialized having aplurality of empty and/or null data fields and/or elements. Forinstance, the information/data in the data transfer object may be usedto populate one or more data fields and/or elements of the new SBRO. Forexample, the subject entity identifier data field and/or element of thenew SBRO may be populated with the subject entity identifier of the datatransfer object. Various other information/data and/or metadatacontained in and/or provided by the data transfer object may be used topopulate various other data fields and/or elements of the new SBRO. TheSBRO processing module 265 may then provide the new SBRO to the DSML 245and request the DSML 245 to store the new SBRO in the ER data store 211in association with the ER corresponding to the subject entity. In anexample embodiment, the SBRO processing module 265 may pass an API call(e.g., API request) comprising and/or identifying the new SBRO andrequesting the new SBRO be written to persistent storage (e.g., in theER data store 211). The SBRO processing module 265 may also cause one ormore change logs, update logs, and/or the like to be updated withmetadata corresponding the new SBRO and the storing of the new SBRO tothe ER data store 211 in association with the ER corresponding to thesubject entity.

At step/operation 1610, since there is an existing SBRO corresponding tothe same health item, event, and/or transaction as the data transferobject in the ER corresponding to the subject entity, the SBROprocessing module 265 evaluates the data transfer object in light of theSBRO and/or the information/data currently stored/contained by the SBRO.The SBRO process 1600 evaluates the semantic resolution, temporalresolution, and resolution of provenance and reliability (e.g., possiblybased at least in part on the source system 40 that generated/createdthe message resulting in the data transfer object and/orinformation/data stored in the SBRO) of the information/data of the datatransfer object compared to the information/data of the existing SBRO.For instance, with respect to the resolution of provenance andreliability, the SBRO processing module 265 evaluates the reliability ofthe information/data elements (e.g., based at least in part on thecorresponding source system 40). For example, an indirect patient reportof a laboratory result has a different level of imputed reliability thanthat same information/data directly reported by the laboratory system.In various embodiments, the SBRO processing module 265 may determineand/or evaluate whether the information/data of the data transfer objectis more complete, more detailed, more reliable, and/or otherwise“better” than the information/data of the existing SBRO.

At step/operation 1612, it is determined whether the SBRO (e.g., one ormore data fields and/or elements of the SBRO) is to be updated based atleast in part on the information/data of the data transfer object, basedat least in part on the evaluation of the information/data of the datatransfer object, based at least in part on the existing SBRO. Forinstance, the SBRO processing module 265 may determine, based at leastin part on the evaluation of the information/data of the data transferobject based at least in part on the existing SBRO, whether the SBRO isto be updated based at least in part on the information/data of the datatransfer object. For example, if one or more elements of theinformation/data of the data transfer object is determined to be“better” (e.g., more detailed, more complete, more reliable, and/or thelike) than the corresponding one or more elements of information/data ofthe SBRO, then the SBRO processing module 265 may determine that theSBRO (e.g., the one or more elements of information/data of the SBRO)should be updated based at least in part on the corresponding one ormore elements of information/data of the data transfer object. When, atstep/operation 1612 it is determined that SBRO should not be updatedbased at least in part on the data transfer object, the SBRO processingmodule 265 instance that received the data transfer object ends and theSBRO is not updated. In an example embodiment, the SBRO processingmodule 265 may cause a change log to be updated to indicate that theSBRO was not updated based at least in part on the data transfer object.

At step/operation 1612, when it is determined that the SBRO should beupdated based at least in part on the data transfer object, the processcontinues to step/operation 1614. At step/operation 1614, the SBRO isupdated and the updates to the SBRO are written to persistent storage bythe DSML 245 as described herein. For instance, the SBRO processingmodule 265 may update one or more elements of the SBRO accessed from theER data store 211 (e.g., loaded into cache) and/or cause the one or moreelements of the SBRO accessed from the ER data store 211 (e.g., loadedinto cache) to be updated. In an example embodiment, updating the SBROmay include updating and/or modifying one or more persistent flags,persistent collections, modified flags, and/or modified collectionsassociated with the updated and/or modified elements of the SBRO. Forexample, the persistent flag corresponding to each data field and/orelement of the SBRO may indicate whether or not the data field and/orelement of the SBRO has already been written to persistent storage ornot. For instance, the persistent collection may indicate which of thedata fields and/or elements of the SBRO have already been written topersistent storage. For example, the modified flag corresponding to eachdata field and/or element of the SBRO may indicate whether or not thedata field and/or component of the SBRO has been updated and/or modifiedbased at least in part on the corresponding information/data element ofthe data transfer object. For instance, the modified collection mayindicate which of the data fields and/or elements of the SBRO have beenupdated and/or modified based at least in part on the corresponding datafields and/or elements of the data transfer object. The SBRO processingmodule 265 may then invoke the DSML 245 (e.g., via one or more API calls(e.g., API requests)) to cause any changes and/or modifications to theSBRO to be stored in persistent storage (e.g., in the ER data store211). In various embodiments, the SBRO processing module 265 may updatea change log and/or cause a change log to be updated to indicate thechanges and/or modifications made to the SBRO.

At step/operation 1616, the SBRO processing module 265 may optionallycause a confidence score for one or more elements of the SBRO to bedetermined or predicted and stored in association with the SBRO. Forexample, the SBRO processing module 265 may cause a confidence scoreengine 235 to determine a confidence score (as discussed with regard toprocess 1800) for one or more elements of the SBRO (e.g., one or moredata fields and/or elements). The SBRO processing module 265 and/or theconfidence score engine 235 may cause the DSML 245 to store theconfidence score in association with the SBRO in the persistent storageof the ER data store 211.

After the SBRO processing module 265 finishes processing a data transferobject, the data transfer object may either be stored in someembodiments and discarded in other embodiments.

Embodiments of the present invention provide technical improvements toautomated managing, ingesting, monitoring, updating, and/orextracting/retrieving of information/data of an ER data store 211 byenabling the ER system 100 to determine the best (e.g., most complete,most reliable, and/or the like) information/data available to the ERsystem 100 corresponding to an event and/or transaction and storing thatbest information/data for user access.

Programmatic Reasoning Logic

Once the expressions (e.g., descriptions/definitions/relationships) ofthe graph-based ontology concepts and/or classes defined by thegraph-based ontology have been created, programmatic reasoning logic 230can take the expressions and arrange them in the correctmulti-dimensional classification based at least in part on theirstructures (properties). For instance, the programmatic reasoning logic230 may generate/create (or access thereafter) a graph data structurerepresenting the expressions of the graph-based ontology concepts and/orclasses. For example, FIGS. 5A, 5B, and 5C illustrate example slices ofthe graph data structure. In the albuterol case, the programmaticreasoning logic 230 (rather than a human modeler) deals with all aspectsof the albuterol concept, such as the active ingredients, formulation,administration route, and/or the like.

The programmatic reasoning logic 230 can consider all of the detail toextract/retrieve all relevant information/data about a concept currentlyinvolved in an ER operation, such as in the ingestion process 1300and/or SBRO process 1600, for example. In the regard, the programmaticreasoning logic 230 can be used to determine whether an assignedontology concept code in an OPDO and/or a DAPDO is accurate.Additionally or alternatively, the programmatic reasoning logic 230 canbe used to determine an assigned ontology concept code from an OPDOand/or a DAPDO when one is not provided.

FIG. 5C shows some of the “anonymous ancestors,” parents, and associatedaxes that inherit into “chronic obstructive pulmonary disease”—differentdimensions that automatically attach every time the concept of COPD isinvoked. Not surprisingly, one finds association with Shortness ofBreath; the disease occurs in the lungs and in the thorax; there is anassociation with a patient-reported or provider-described discomfort ordifficulty in breathing, and/or the like. The beauty of the environmentis that none of these things need to be remembered or referenced by ahuman every time COPD is considered—with the graph-based ontology, thishappens automatically. In short, the modularization of the human task,coupled with the mathematical rigor of the programmatic reasoninglogic's 230 examination of every possible connectoid in the graph-baseddomain ontology creates deep stability. Concepts and class expressionsthat are reasoned appropriately and named can be reused safely andindefinitely. Parts of the ER system 100 that have been settled can beleft alone—this is why changes to the core tend to be infrequent afterprocessing a large number of messages (e.g., a hundred thousand to onebillion or more messages). Thus, in various embodiments, theprogrammatic reasoning logic 230 is executed prior to the processing ofa particular message and the results of processing the programmaticreasoning logic 230 are stored (e.g., in cache) and used to interpretand/or perform reasoning tasks with relation to the processing of aparticular message and the updating of an ER based thereon. For example,the programmatic reasoning logic 230 may execute to generate/create (oraccess thereafter) a graph data structure, and the graph data structuremay be stored (e.g., in cache) such that the graph data structure doesnot need to be re-generated during the processing of each message. Forexample, the ER system 100, prior to runtime, may execute every possiblequery to the ontology and store the corresponding responses in a cachefor increased efficiency.

Further, an importantly for large, complex users, principled, safeextensions to the graph-based domain ontology can be made to deal withunusual cases without causing any erosion of other already-testedcomponents of the environment. For instance, relationships betweenontology categories and/or concepts not defined in the graph-baseddomain ontology and classes and/or concepts defined in the graph-basedontology may be reasoned and/or determined by the programmatic reasoninglogic 230 (e.g., using the cached responses and/or the pre-generatedgraph data structure).

Having the programmatic reasoning logic 230 perform the task ofgenerating the graph data structure and reasoning against the sameprovides significant technical advantages. It is now possible to makegeneralized statements about concepts and have the computing entity dealwith all the consequences of those statements. For example, from asingle statement that an active ingredient has a certain therapeuticeffect, the programmatic reasoning logic 230 will ensure that allmedications comprising that active ingredient are placed in theappropriate therapeutic group. This results from the application ofdescription logic by the programmatic reasoning logic 230 and does notrequire a person to attempt to do it by hand for each of thousands ofconcepts. One consequence of this is that the traditional notion of“level” (such as level of a hierarchy) as a fixed thing is neitherrequired nor supported. Rather, each concept is classified in as manydimensions as it has properties. Rather than ask about locations inhierarchies, we ask ‘what is known about this concept’ both directlystated and inferred by the programmatic reasoning logic 230. Theinformation system can choose to “view” the concept from a particularperspective at any instant in time, but every such view pulls throughthe relationships to all other views simultaneously. As importantly, thechoices of such views or operational states are no longer frozen atconstruction.

In several embodiments, the present invention includes thegenericization of canonicity. In the context of ontology work,“canonical” means the “canonical form of an expression.” It is concernedwith how things are represented in formal systems of logic, such asthose used in the present system, and canonicity can be pivotal to theproper performance of such environments. Canonical means the single formto which an expression may canonize. It does not necessarily mean thatform is “correct” in any external, human sense of meaning.

The “Canonical Form” arises in representations that allow concepts to becombined through relationships to generate/create formal definitions ofother concepts. It comprises how to deal with there being multiple waysof expressing what is the same thing. For instance, if there areconcepts for Fracture and Humerus, and a relationship hasLocation, andthere are rules for combining them, then the combination can begenerated/created (Fracture that hasLocation Humerus). This could bedefined as “meaning” fracture of the humerus. In this example, Fractureand Humerus are primitive concepts. By defining things, they can nowhave a set of richer formal meanings which can be more useful.

If concepts are now added for severity and other parts of long bonessuch as “shaft,” then more formally defined concepts can begenerated/created:

Expression Interpretation Shaft that isPartOf Humerus “shaft of thehumerus” Fracture that hasSeverity severe “severe fracture” Fracturethat hasSeverity severe and “severe fracture of the humerus” hasLocationHumerus

The issue of “canonical” arises when there are multiple ways ofexpressing the same thing, including elements of a description that areredundant. For example, Fracture that hasLocation Humerus andhasLocation Bone clearly has a redundant element if, in the graph-basedontology, Humerus is a kind of Bone. The canonical form is derived usingrules that embody this type of situation. Thus, one would expect thecanonical form of: Fracture that hasLocation Humerus and hasLocationBone to be Fracture that hasLocation Humerus. The situation gets morecomplex, however, when complex expressions are embedded in otherexpressions. For instance, “fracture of the shaft of the humerus” couldbe: Fracture that hasLocation (Shaft that isPartOf Humerus) or Fracturethat hasLocation Humerus and hasLocation (Shaft that is Part OfHumerus). Further, Fracture that hasLocation Shaft and hasLocationHumerus would be expected to resolve all to a canonical form Fracturethat hasLocation (Shaft that isPartOf Humerus). Likewise, whenintroducing concepts of left and right laterality, a “left-sidedfracture of the humerus” must end up the same as “a fracture of the lefthumerus.” Dealing with such issues is non-trivial and requiressophisticated information/data science.

These examples are practical, and arise, for instance, when dealing withinformation/data entry. A user may select the condition (fracture) firstand then the site, or the site and then the condition, or (because usersare human) some mix of the two. In a graph-based ontology, there areseveral “routes” to the same concept, so it is imperative that the sameconcept eventuates irrespective of the route taken to get there (e.g.,traversal of nodes and edges in the directed acyclic graph datastructure). Current systems and environments do not consider theseissues or do not treat them with formal rigor, resulting in amultiplicity of ways the same information/data is stored. This createsthe well-recognized issues in attempting to query or act intelligentlyon such information. Thus, previous systems fail or do not attempt anindividual patient focus.

Almost all attempts to be truly “canonical” are flawed by notunderstanding the above. SNOMED is an example of this. For example,SNOMED began by creating a separate SNOMED code for every definition itintended to be used, thereby hoping to avoid the canonical form issue.However, if separate codes are generated/created for every possiblecombination, the graph-based ontology becomes so large that it defeatsthe purpose of having a compositional scheme.

Embodiments of the present invention comprise a fully modeled conceptfor every kind of unit/numeric value and are dealt with generically andwith minimal computational requirements. To be comparable,information/data must be expressed in a canonical form. Any kind ofmeasurement information/data, such as concentration of substance in aspecimen (e.g., glucose 100 mg/dL), physiological function measurements(e.g., diastolic blood pressure 93 mmHg), currency observations (e.g.,paid amount $100 USD), strength of a medication (e.g., amoxicillin 500mg) or administered dose (e.g., 2 tablets) is expressed in the ER system100 in canonical form. Such information/data is represented as ameasurement data object composed of a “role” (what) and a quantity whichis expressed as a discrete numeric value and unit of measure.

A model example of some measurements expressed in theformal-description-logic-based canonical form (role-value-unit) is:

311668 Metoclopramide 5 MG Oral Tablet  TabletDoseForm  and(hasActiveingredient some   (MetoclopramideHydrochloride   and(hasStrength some    (Quantity    and (hasUnit some milligram)    and(hasMagnitude value 5.00))))   and (hasDoseQuantity some    (Quantity   and (hasUnit some TabletForm)    and (hasMagnitude value 1.00))   and(hasMedicationRoute some OralingestionRoute)   and (hasUnitDoseMeasuresome TabletForm)   and (hasActiveingredient only  (MetoclopramideHydrochloride   and (hasNumerator some    (Quantity   and (hasUnit some milligram)    and (hasMagnitude value 5.0f)))))

In this example, the canonical representation is important. However, itis difficult to determine the appropriate values from patientinformation/data received when the information/data is incomplete. Inthe healthcare context, the standard of practice in medicine is toinclude the unit of measurement along with any clinical measurement.Whether a result is associated with a glucose value on a blood sample, ablood pressure taken in the doctor's office, a bone length measured byimaging, or even an age, there is an expectation that theinformation/data value associated with the observation will comprise itscorresponding unit of measure as it is required to accurately understandthe meaning of the information/data.

Unfortunately, not all clinical settings and not all systems report anobservation's unit of measure. Often, no unit of measurement isprovided, the unit of measure is asserted in the observation conceptitself (e.g., the name of the result) instead of in the unit's field, orthe unit does not correlate to the information/data value. Peoplereading information/data on a handwritten record or a computing entityscreen display often infer the unit based at least in part on theirknowledge of what is sensible or physiologically possible. While theymay be right, often they are not. Unfortunately, current systems cannotde novo make those kinds of inferences and cannot subsequently processthe information/data value in the ways desired. Such non-categorizedinformation/data cannot be correlated to other like information/datavalues for display purposes, such as trending and graphing. Similarly,it appropriately be used in automated decision support processes (e.g.,rules) or in accurate reporting.

The ER system 100 can perform unit inference based at least in part onvalue characteristics. For some information/data values, the unit ofmeasurement corresponds to a mutually exclusive set of value ranges ifthe characteristics of the information/data elements are known. Forexample, analyte observations are most commonly seen in the lab andtypically measure the amount of a substance in a specimen. If thenumeric analyte value falls within a certain range, the unit can beinferred. For instance, a unit mg/dL can be inferred for a glucose valueof 101 (valid range>=25), whereas mmol/L can be inferred for a value<10.This kind of modeling requires an understanding of the kinds of testingthat can be done for glucose and the potential range of results and themodeling of the measurement concepts to deal with them automatically andappropriately. Similarly, body temperatures in a clinical setting aretypically reported in Fahrenheit and/or Celsius. The scales of these twomeasurement schemes are mutually exclusive relative to the valuesobserved for living persons; therefore, the measurement concepts“understand” that if <45 infer the unit as Celsius and if >85 infer thevalue as Fahrenheit (assuming the patient/subject entity is alive).

The ER system 100 also can perform unit inference based at least in parton age and gender-specific value considerations. For someinformation/data values, the unit of measurement is modeled toincorporate a set of age-specific value parameters. The age of thepatient is a factor to help determine which set of values apply; this issimilar to the mechanism used for age-adjusted reference ranges of whichthere are often many, especially in pediatric populations. Two examplesare weight and height.

Weights are commonly reported in kilograms and/or pounds in a clinicalsetting. Thus, for example, the ER system 100 may receive weight valuesof 25.25 and 11.48, both without any units, on a 19-month old patientfrom two visits (to different offices) on the same day. Usinggender-specific and situation-specific growth chart information/dataincorporated in the model as a reference to provide age-gender relatedranges, the 11.48 value fits within the male 18 month age range of 9.5kgs. (20.9 lbs.) to 14.3 kgs (31.5 lbs.), and thus would infer a unit ofkgs. The measurement of 25.25 would infer a unit of lbs., also withinthe appropriate range.

Similarly, heights are commonly reported in inches and/or centimeters ina clinical setting. Thus, for instance, the ER system 100 may receiveheight values of 32.5 and 82.5 for the 19-month old patient on the sameday. Based at least in part on age-gender growth chart information/datafor males, the 82.5 measure is tagged as centimeters, and the 32.5 valueas inches.

The ER system 100 can also perform unit inference based at least in parton observable definition of unit inclusion. In some cases, the unit ispart of the description of the information/data label such as “Weight(lbs.).” In these cases, the interface definition uses the “observable”that includes the unit in its definition. This provides all of theappropriate parsing and definition of the incoming information/data tothe proper concepts; thus, all of the rest of the proper management ofthe unit-of-measure from that point forward is handled automaticallyacross all operations. Everything becomes a modeled concept, includingunits and measures. They then can be fully incorporated into theknowledge model (e.g., the graph-based domain ontology).

As described, observables provide a simplified, external view used foringesting information/data (e.g., items) into the ER system 100. Theyprovide a simple, real-world link from things that can be seen directlyto the complete knowledge base of the present invention, including butnot limited to the information/data model, the knowledge model (e.g.,the graph-based domain ontology), and the SBRO model. The modules andservices described herein provide the services to accept inboundinformation/data from various source systems 40, including sourcesystems with pre-existing information/data. The modules and servicesmanage the processing of the source system information/data, inconjunction with OPDOs linkage and/or DAPDO linkage (relationship)definition to generate/create OPDOs and/or DAPDOs to be processed forpersisting the information/data stored therein. As described previously,OPDOs and/or DAPDOs may comprise observations, which are key-value pairs(e.g., an observable and a source information/data value). Eachobservable may have connections and relationships to many concepts andinformation/data model (e.g., graph-based domain ontology) components.Observables minimize the amount of the underlying infrastructure thatmust be known when completing a connectoid.

From a process perspective, once a message (e.g., message 900) isreceived by the ER system 100, the message's observable packetdefinition is used to parse the message and generate/create an OPDOcomprising the information/data values and their associated observable,which is then used to generate/create a corresponding DAPDO (either bythe message pre-processing module 220 or the ingestion processing module225)ss. As previously described, corresponding event/activity setsprovide the context of the information/data comprised in the messages.Each event/activity has its own set of observables uniquely related toits context (a one to many mapping). For example, the context of asubject entity who is the subject of an eligibility event/activitymessage is an “insured” person, whereas the context of a subject entitywho is the subject of a visit event/activity message is a “patient.” Theevent/activity selected for a given interface definition limits theobservables available for data field-defining, so only those conceptswith the appropriate context are available for selection. Thus, thedefinition of an observable comprises the event/activity to which it isrelated, the type of entity it observes, the role of that entity, theaspect of the entity observed, the data type that describes the formatof the information/data, and/or the like.

The ER system 100 begins the process by using the DAPDO togenerate/create a container tree data structure. As discussed withregard to the ingestion processing module 225, the arrangement ofobservations (e.g., key-value pairs or observable-value pairs) intogroupings of container nodes (e.g., having a data artifact packetcontainer node as the root of the container tree data structure) and/orsubtree data structures. Each container node comprises a collection ofrelated observations (e.g., all demographic observations about thesubject entity who is the patient, all observations about theperson/entity who is the attending provider, all observations about agiven procedure performed, and the like). The container tree datastructure is an ordered arrangement of observation container nodes basedat least in part on the definition of each observable. The observabledefinition identifies what entity it observes (e.g., a person/entity whois the patient/subject entity, a person who is the attending physician).The entity and role are interpreted to generate/create a container.

The ER system 100 then starts the transformation (e.g., assembly ofcontainer nodes of the container tree data structure to form datatransfer objects) by processing each container node in the containertree data structure. Each observation in a container is interpretedbased at least in part on the definition of the observable. Thedefinition of the observation role in the observable's definitionfurther elaborates the meaning of the information/data associated withan observable. Each observation role's class/ontology concept expressioncomprises information/data used to describe the information/dataassociated with its related observable. The observation role identifiesthe relationships and characteristics used to both represent and expressthe information/data received in a message. Two message data fields maybe combined to generate/create a single observation; for example,separate date and time fields each mapped to separate observables, onewith a data type of date and another with a data type of time arecombined to generate/create the value of a “health item” timestamp(e.g., date/time). Cardinality characteristics of the observation rolemay affect the behavior of the SBRO processes causing multiple values tobe associated with an instance of a multicardinality observation and mayreplace an existing value of a single-cardinality observation role of anexisting SBRO. The observation type of a given observation roleidentifies the aspect being observed, and in turn, identifies othercharacteristics and expected relationships of the information/data.

For instance, an observation type of substance concentration defines anexpectation of a semi-quantitative or quantitative value, with thelatter being a measurement (e.g., composed of number and associatedunit). The observable data type is used to process the information/datavalue. For example, telephone string fields invoke telephone validationprotocols, vocabulary code designations are used to invoke code lookupin a given code set, and identifier value data types allow theidentifier formats to be consistently normalized. Separate observationsare related to one another based at least in part on the observationrole's definition. For instance, independent systolic blood pressure,diastolic blood pressure, body position during blood pressuremeasurement, procedure site of blood pressure measurement, and bloodpressure method are related to one another to generate/create acomposite multi-element observation of blood pressure. This allows anyinformation/data about an instance of a blood pressure measurement to beeasily obtained, eliminating the need to compare the timestamps ofindividual elements to determine which are related.

In one embodiment, and as described above, the programmatic reasoninglogic 230 is configured to generate/create a graph data structure thatcan then be used in processing of a DAPDO (e.g., by the messagepre-processing module 220 and/or ingestion processing module 225),evaluating the data transfer object (e.g., via the SBRO processingmodule 265), and/or the like. In various embodiments, a category orconcept may be identified in a message, DAPDO, and/or data transferobject that is or is not defined in the graph-based domain ontology. Forexample, the category or concept may not be present in the graph datastructure. For instance, an aggregator may be assembling container nodesof a container tree data structure and identify a category or conceptnot defined in the graph-based ontology and/or not present in the graphdata structure. The programmatic reasoning logic 230 may be configuredto determine and/or generate/create a description of the category in theterms of classes defined in the graph-based ontology (also referred toas ontology concepts herein). For example, based at least in part on thesource vocabulary, source vocabulary code, and source description of acategory and/or a value associated with a category, the programmaticreasoning logic 230 may determine and/or generate/create a descriptionof the category in terms known and/or defined by the graph-based domainontology. Based at least in part on the description of the category, theprogrammatic reasoning logic 230 may then determine an appropriatelocation within the graph data structure for the category. For instance,the programmatic reasoning logic 230 may determine one or morerelationships between the category and classes defined by thegraph-based domain ontology such that the category may be, at leastprovisionally, added to the graph data structure. In variousembodiments, multiple ontology classes and/or ontology categories may beidentified through the processing of messages (e.g., by the messagepre-processing module 220, ingestion processing module 225, SBROprocessing module 265, and/or the like). The programmatic reasoninglogic 230 is capable of determining a relationship between two of theseontology categories that are not defined within the graph-based domainontology.

FIGS. 17A and 17B provide flowcharts for exemplary processes 1700Aand/or 1700B (e.g., programmatic reasoning processes 1700A and/or 1700B)illustrating various processes, procedures, steps, operations, and/orthe like performed by the programmatic reasoning logic 230 executed bythe processing element 205 of the server 65 to determine and providerelationships between and among categories defined within a graph-baseddomain ontology, based at least in part on the graph data structurerepresenting the graph-based domain ontology. FIG. 17A represents theprogrammatic reasoning logic 230 providing a concept identifier as aresult of reasoning for a concept defined in the graph-based domainontology. FIG. 17B represents the programmatic reasoning logic 230providing information/data about a concept not defined in thegraph-based domain ontology.

Starting at step/operation 1702A of process 1700A, the programmaticreasoning logic 230 receives a data transfer object from the ingestionprocessing module 225 and/or the SBRO processing module 265. Asdescribed, the data transfer object may comprise a key-value pair for anobservation. The ingestion processing module 225 and/or the SBROprocessing module 265 may also pass the source vocabulary, sourcevocabulary code, and/or source description associated with the datatransfer object.

At step/operation 1704A, the programmatic reasoning logic 230 generatesan ontology-based description for the observation to identify theappropriate ontology class/concept. For example, based at least in parton the source vocabulary, source vocabulary code, and/or sourcedescription corresponding to the ontology class/concept, theprogrammatic reasoning logic 230 may generate/create a description theobservation. For instance, based at least in part on the sourcevocabulary, source vocabulary code, and/or source descriptioncorresponding to the ontology class/concept, the programmatic reasoninglogic 230 may determine whether the observation corresponds to aprocedure, equipment, a diagnosis, therapeutic, and/or the like. If theobservation is a procedure or diagnosis, for instance, the programmaticreasoning logic 230 may use the source vocabulary, source vocabularycode, and/or source description corresponding to the observation todetermine a body part or portion of the body corresponding to theobservation. Thus, through analyzing the source vocabulary, sourcevocabulary code, and/or source description corresponding the observationin light of ontology classes/concepts defined by the graph-basedontology, a description for the observation is generated/created.

In one embodiment, at step/operation 1706A, the programmatic reasoninglogic 230 determines an appropriate ontology class/concept based atleast in part on the description of the observation. To do so, thegraph-based ontology may be queried at runtime. For example, theprogrammatic reasoning logic 230 may determine the ontologyclass/concept for the observation. In that regard, the programmaticreasoning logic 230 executes efficient “matching” logic. To perform thematching, the programmatic reasoning logic 230 defines relatedness anddefines what questions should be asked (e.g., queried) or considered ofthe graph-based ontology. To that end, the programmatic reasoning logic230 uses the terms “equivalent” (two descriptions that refer to the sameconcept regardless of how they are individually stated), “subsumes” (thefirst concept is a broader concept than the second, such as myocardialinfarction is a broader concept than is myocardial infarction due toocclusion of the left coronary artery), “subsumed” (the reverse ofsubsumes), and “unrelated” (two concepts are unrelated in any way whichis meaningful within the problem domain). Further, the programmaticreasoning logic 230 asks (queries) at least the following. How isconcept A related to concept B? What are all of the members of the setof medically meaningful events which are related to A in a particularway (this question relies on a repetitive use of the first question)?

Once the programmatic reasoning logic 230 has a set of candidates basedat least in part on concept relatedness, it filters the set based atleast in part on secondary considerations such as date of onset,resolution status, acuteness, and/or the like. Additionally, theprogrammatic reasoning logic 230 also asks (e.g., queries) at least thefollowing. What are all the defined children/descendants of a specificconcept? What are all of the variants of a specific concept with respectto a particular axis or axes?

In order to meet complex matching needs, the programmatic reasoninglogic 230 possess at least the following language elements: subclassOf,AND, OR, SOME, MIN, MAX, VALUE, optionally ONLY. With the above, theprogrammatic reasoning logic 230 is able to draw inferences based atleast in part on the above language elements. Additionally, theprogrammatic reasoning logic 230 supports least the following features:the ability to determine relationship status of two arbitrary conceptdescriptions with respect to a given ontology; the ability to perform“incremental” reasoning (e.g., the ability to add concepts to anexisting ontology without having to recomputed all inferences); theability to construct its internal graph directly from a pre-reasonedontology; computational efficient (e.g., the ability to find allelements of a set matching a particular concept in a very small numberof milliseconds).

Because of the deficiencies in existing systems, the programmaticreasoning logic 230 supports class reasoning, not necessarily individualor property reasoning. For example, a “simple class” is an ontologyclass or an intersection (“and”) or union (“or”) which flattens to asingle ontology class. An “undefined class” is a simple class that hasno equivalent or subclass expressions. A primitive class is an undefinedclass or a simple class that has only defining class expressions whichare subclasses of primitive classes. An “expanded form” is a descriptionthat is completely expanded such that all declared references to otherclasses are replaced with the corresponding descriptions and attributesmerged so that the description appears in its most primitive possibleform. For example, if A=ER_Concept and hasX some X, and B=A and hasYsome Y, then the expanded form of B=ER_Concept and hasX some X and hasYsome Y. And “singleton” is a single expression element or if theexpression element is an “intersection” or a “union” and has a singleoperand which is a singleton.

In one embodiment, the programmatic reasoning logic 230 arranges a setof concepts as declared in an ontology into the graph data structuresuch that each node in the graph contains concepts that are morespecific than—or are subsumed by—those in the nodes above and that areless specific than—or subsumes—those in the nodes below.

In one embodiment, the graph searching (querying) process relies on theability of the programmatic reasoning logic 230 to compare thedescriptions of two concepts. The comparison process is the resolutionof two separate one-way equivalence—or—subsumption comparisons. Forexample, assume that A and B represent two concept descriptions. Let R1be true if A is equivalent to or subsumes B; otherwise false. Let R2 betrue if B is equivalent to or subsumes A; otherwise false. If R1 and R2are both true, then A and B are equivalent. If R1 is true but R2 isfalse, then A subsumes B. If R1 is false but R2 is true, then B subsumesA. If R1 and R2 are both false, then A and B are unrelated.

The equivalence—or—subsumption comparison begins with an ontology classand proceeds briefly as follows. If A and B are both classes and are thesame class, then return true. If the ontology from which the conceptsare derived asserts that A subsumes B, then return true. If A and B areboth primitive, then return false. If A is directly or indirectlyreferenced as a by a <class> clause of B's description, then returntrue. If A is equivalent of subsumes B by manual inspection of thedescriptions, then return true. Return false.

Continuing with the above, assume that P1 is a piece of the expandeddescription of A and that P2 is a piece of the expanded description ofB, then satisfaction by P1 piece type is determined as follows forsubclassOf: (1) If P2 is simple then: If P1 is equivalent to or subsumesP2 converted to simple form then return true. Return false. (2) If P2 isof type “intersection” then: Decompose P2 into “and” pieces. If anypiece of P2 satisfies P1 then return true. Return false. (3) If P2 is oftype “union” then: Decompose P2 into “or” pieces. If all pieces of P2satisfy P1 then return true. Return false.

Continuing with the above example, the following is a non-limitingexample comparison for MIN: (1) If P2 is of type “min” then: If theproperty associated with P1 is not equivalent to and does not subsumethe property associated with P2 then return false. If the valueassociated with P1 is less than or equal to the value associated with P2other pieces value, then return true. Return false. (2) If P2 is of type“intersection” then: Decompose P2 into “and” pieces. If any piece of P2satisfies P1 then return true. Return false. (3) If P2 is of type“union” then: Decompose P2 into “or” pieces. If all pieces of P2 satisfyP1 then return true. Return false.

Continuing with the above example, the following is a non-limitingexample comparison for MAX: (1) If P2 is of type “max” then: If theproperty associated with P1 is not equivalent to and does not subsumethe property associated with P2 then return false. If the valueassociated with P1 is greater than or equal to the value associated withP2 other pieces value, then return true. Return false. (2) If P2 is oftype “intersection” then: Decompose P2 into “and” pieces. If any pieceof P2 satisfies P1 then return true. Return false. (3) If P2 is of type“union” then: Decompose P2 into “or” pieces. If all pieces of P2 satisfyP1 then return true. Return false.

Continuing with the above example, the following is a non-limitingexample comparison for VALUE: (1) If P2 is of type “value” then: If theproperty associated with P1 is not equivalent to and does not subsumethe property associated with P2, then return false. If the valueassociated with P1 is equal to the value associated with P2 other piecesvalue, then return true. Return false. (2) If P2 is of type“intersection” then: Decompose P2 into “and” pieces. If any piece of P2satisfies P1 then return true. Return false. (3) If P2 is of type“union” then: Decompose P2 into “or” pieces. If all pieces of P2 satisfyP1 then return true. Return false.

Continuing with the above example, the following is a non-limitingexample comparison for SOME: (1) If P2 is of type “some” then: If theproperty associated with P1 is not equivalent to and does not subsumethe property associated with P2, then return false. If the valueassociated with P1 is equivalent to or subsumes the value associatedwith P2, then return true. Return false. (2) If P2 is of type“intersection” then: Decompose P2 into “and” pieces. If any piece of P2satisfies P1 then return true. Return false. (3) If P2 is of type“union” then: Decompose P2 into “or” pieces. If all pieces of P2 satisfyP1 then return true. Return false.

Continuing with the above example, the following is a non-limitingexample comparison for INTERSECTION (“AND”): (1) If P2 is of type“intersection” then: Decompose P1 and P2 into “and” pieces. If everypiece of P1 is equivalent to or subsumes some piece of P2 then returntrue. Return false. (2) If P2 is of type “union” then: Decompose P2 into“and” pieces. If any piece of P2 satisfies P1 then return true. Returnfalse. If P1 is a singleton then: If P1 converted to singleton form isequivalent to or subsumes P2 then return true. Return false.

Continuing with the above example, the following is a non-limitingexample comparison for UNION (“OR”): (1) If P2 is of type “union” then:Decompose P2 into “or” pieces. If P1 is equivalent to or subsumes allpieces of P2 then return true. Return false. (2) If P2 is of type“intersection” then: Decompose P2 into “and” pieces. If any piece of P2satisfies P1 then return true. Return false. (3) Otherwise: Decompose P1into “or” pieces. If some piece of P1 is equivalent to or subsumes P2then return true. Return false.

As will be recognized then, based at least in part on the description ofthe observation, the programmatic reasoning logic 230 may identifyontology classes/concepts that are similar to and/or connected to thedescription of the observation. For example, based at least in part onthe description of the observation, the programmatic reasoning logic 230may determine and/or reason relationships between the description of theobservation and one or more ontology classes/concepts defined by thegraph-based ontology. If successfully identified, the programmaticreasoning logic 230 identifies the appropriate ontology class/conceptalong with its corresponding concept identifier and returns the same. Ifnot successfully identified, the programmatic reasoning logic 230returns false.

At step/operation 1708A, the programmatic reasoning logic 230 determineswhether a concept identifier or “false” was returned as a result of thequery. When a concept identifier is returned as a result of the query,at step/operation 1710A, the programmatic reasoning logic 230 providesthe concept identifier for storage in association with the data transferobject (and/or to other processes and/or modules). When false isreturned, at step/operation 1712A, the programmatic reasoning logic 230proceeds to step/operation 1702B of process 1700B.

As discussed above, the programmatic reasoning logic 230 can be invokedat various times and by various modules and processes. For example, atstep/operation 1604 of the SBRO process 1600, the SBRO typecorresponding to the data transfer object needs to be determined basedat least in part on the graph-based ontology. At this step/operation,the SBRO processing module 265 may invoke programmatic reasoning logic230 and/or otherwise cause programmatic reasoning logic 230 to determinean SBRO type corresponding to the data transfer object. For example, theprogrammatic reasoning logic 230 (e.g., via process 1700A) may analyzeontology concept identifiers (and/or their corresponding descriptionsand/or metadata) present in the data transfer object to identify and/ordetermine an SBRO type corresponding to the data transfer object. Aspreviously described, an SBRO may correspond to an item (e.g., a healthitem), a relationship (e.g., between two entities), subject entityidentifying information/data (e.g., name, birthdate, social securitynumber, insurance member identification number, and/or the like),subject entity contact information/data (e.g., address, telephonenumber, email, and/or the like), and/or other ontology concepts and theSBRO type indicates the ontological concept corresponding to the SBRO.For instance, the data transfer object may correspond to the ontologicalconcept (with a corresponding concept identifier) of diastolic bloodpressure (with an appropriate value in the range of 0 to 110). In thecase, the programmatic reasoning logic “reasons” against the ontology todetermine that ontological concept corresponding to the data transferobject is for diastolic blood pressure. As a result, the programmaticreasoning logic 230 can return (to the SBRO processing module 265) theconcept identifier for diastolic blood pressure, an allowable range ofvalues, and/or a description for the ontology concept. With the conceptidentifier for diastolic blood pressure, an allowable range of values,and/or a description for the ontology concept, the ingestion processingmodule 225 and/or the SBRO processing module 265, can invoke the DSML245 to request retrieval/loading of the SBRO for the entity stored inthe ER data store 211. The DSML 245 will execute the request asdescribed herein.

As will be recognized, the graph-based domain ontology can be queried atruntime. However, because queries against the graph-based ontology areresource intensive and time intensive, the programmatic reasoning logic230 may pre-execute all possible queries and the corresponding resultsin a cache for efficient access during data ingestion processes. Thisallows the programmatic reasoning logic 230 to execute queries againstthe cache for increased computational efficiency.

If process 1700A returns false, the programmatic reasoning logic 230 canproceed to process 1700B. In process 1700B, at step/operation 1702B, theprogrammatic reasoning logic 230 provides the data transfer object, thenull response, and/or information/data corresponding to the observationto a new process or a subprocess. Then, at step/operation 1704B, theprogrammatic reasoning logic 230 generates a new description for theobservation. For example, based at least in part on the sourcevocabulary, source vocabulary code, and/or source description, theprogrammatic reasoning logic 230 may generate/create a new descriptionfor the observation to generate/create a first new ontology category forthe observation in the graph-based ontology vocabulary as describedherein previously.

At step/operation 1706B, the programmatic reasoning logic 230 determinesan appropriate location/position for the first new ontology categorywithin the graph data structure. For example, the programmatic reasoninglogic 230 may determine that the appropriate location/position withinthe graph data structure for the first new ontology category is a firstlocation/position. For instance, based at least in part on thedescription of the first new ontology category in the graph-basedontology vocabulary, the programmatic reasoning logic 230 may identifyfirst ontology classes/concepts that are similar to and/or connected tothe first new ontology category. For example, based at least in part onthe new description of the first new ontology category in thegraph-based ontology vocabulary, the programmatic reasoning logic 230may determine and/or reason relationships between the first new ontologycategory and one or more ontology classes/concepts of the plurality ofontology classes/concepts defined by the graph-based ontology.

At step/operation 1708B, the programmatic reasoning logic 230 receivesinformation/data corresponding to a second new ontology category. Forinstance, the ingestion processing module 225 (e.g., an aggregator) mayidentify a second new ontology category when assembling a containernode, wherein the second new ontology category is not defined in thegraph-based domain ontology. For example, the second new ontologycategory may be distinct from the ontology classes/concepts defined bythe graph-based domain ontology.

At step/operation 1710B, the programmatic reasoning logic 230 generatesa description of the second new ontology category. For example, based atleast in part on the source vocabulary, source vocabulary code, and/orsource description, the programmatic reasoning logic 230 maygenerate/create a description for the second new ontology category.

At step/operation 1712B, the programmatic reasoning logic 230 determinesan appropriate location/position for the second new ontology categorywithin the graph data structure. For instance, the programmaticreasoning logic 230 may determine that the appropriate location/positionwithin the graph data structure for the second new ontology category isa second location/position. For example, based at least in part on thedescription of the second new ontology category in the graph-basedontology vocabulary, the programmatic reasoning logic 230 may identifysecond ontology classes/concepts that are similar to and/or connected tothe second new ontology category. For instance, based at least in parton the description of the second new ontology category in thegraph-based ontology vocabulary, the programmatic reasoning logic 230may determine and/or reason relationships between the second newontology category and one or more second ontology classes/concepts ofthe plurality of concepts/ontology classes defined by the graph-basedontology.

At step/operation 1714B, the programmatic reasoning logic 230 may reasonand/or determine a relationship between the first new ontology categoryand the second new ontology category. For example, based at least inpart on the first location/position in the graph data structuredetermined for the first new ontology category and the secondlocation/position in the graph data structure determined for the secondnew ontology category, the programmatic reasoning logic 230 may infer,and/or determine a relationship between the first new ontology categoryand the second new ontology category. For instance, the programmaticreasoning logic 230 may determine if the first and second ontologycategories have a parent-child relationship, sibling relationship, aresimilar, are different, a degree of difference (e.g., degree ofseparation between the first and second ontology categories), and/or thelike.

At step/operation 1716B, the programmatic reasoning logic 230 may carryout several actions. For example, provide the relationship reasonedand/or determined between the first new ontology category and the secondnew ontology category. This may be referred to as “incremental”reasoning. Further, the programmatic reasoning logic 230 may cause oneor more DBO definitions to be updated, based at least in part on thereasoned and/or determined relationship between the first new ontologycategory and the second new ontology category. For instance, theprogrammatic reasoning logic 230 may return the reasoned and/ordetermined relationship between the first new ontology category and thesecond new ontology category to the ingestion processing module 225 foruse in assembling container nodes of a container tree data structure. Inparticular, the programmatic reasoning logic 230 may provide thereasoned and/or determined relationship between the first new ontologycategory and the second new ontology category to another componentand/or process of the ER system 100 such that the reasoned and/ordetermined relationship may be used in the processing of messages,managing, ingesting, monitoring, updating, and/or extracting/retrievingof the ER data store 211, and/or the like. Further, the programmaticreasoning logic may insert the first new category and second newcategory in the graph data structure. To do so, the programmaticreasoning logic 230 performs a depth-first traversal of the graphsearching for the lowest node in the graph at which each new categorycontinues to subsume those classes/concepts under it. Second, when sucha node is found, programmatic reasoning logic 230 determines if thecorresponding new category is equivalent to classes/concepts at thatpoint. If the corresponding new category is equivalent, programmaticreasoning logic 230 adds the corresponding new category to the foundnode. If the concept is not equivalent, programmatic reasoning logic 230creates a new node and inserts it into the graph data structure. If anew graph node was created, programmatic reasoning logic 230 searchesfor any additional nodes in related branches of the graph which areproperly children of the new node and adds them as children.

Various embodiments provide technical solutions to technical problemscorresponding to reasoning logic and reasoners for ontologies. Inparticular, the execution of reasoning logic tends to be resourceintensive and time intensive. For example, executing the programmaticreasoning logic 230 during the data ingestion processes to continuallyquery the graph data structure would significantly slow down the dataingestion processes and/or would require significantly morecomputational resources. However, due to the stability of thegraph-based ontology, the programmatic reasoning logic 230 maypre-execute all possible queries and the corresponding results in acache for efficient access during data ingestion processes. Embodimentsof the present invention therefore provide technical improvements suchas a reduction in the computationally intensiveness and the timeintensiveness needed to use reasoning logic as part of the automatedmanaging, ingesting, monitoring, updating, and/or extracting/retrievingof information/data of the ER data store 211.

Confidence Score

In one embodiment, a confidence level may be programmatically determinedfor each information/data element of an SBRO. For instance, theconfidence level may be driven by the source of the information/dataelement, how much processing was required for the information/dataelement, and/or the derivation history of the information/data element.For instance, a confidence level is based at least in part on thefollowing information: the information/data source, identificationreliability (e.g., social security number, medical record number, name,and/or the like), derivation of the information/data, and certainty ofthe derived information/data. The confidence level may be the aggregate,the composite, and/or a probabilistic prediction based at least in parton the same. As an example, an information/data element may directlyimply another piece of information/data with absolute certainty. In thisexample, the confidence level of the derived information/data isidentical to the confidence level of the source information/data. Inanother case, it might be that a particular information/data elementimplies another information/data element 95% of the time. In this case,the certainty of the derived information/data element would be 95% ofthat of the original information/data element. Finally, if aninformation/data element is derived from two or more pieces ofinformation/data, then the confidence level may be the statisticalaggregate of the confidence levels of its source multiplied by theconfidence level of its derivation.

In one exemplary embodiment, a confidence level may be discrete for eachinformation/data element. Accordingly, where a confidence level isassociated an information/data element, it is possible to propagateconfidence levels at each step/operation of information/data derivation.Further, this approach also enables the setting of a confidencethreshold below which additional information/data no longer is derived.

In various embodiments, the confidence rating indicates how confidentthe ER system 100 is that the information/data stored within the elementof the SBRO is truthful and/or accurate. Each element of an SBRO maycorrespond to a particular facet of an ER corresponding to a subjectentity. When the ER system 100 receives new information/datacorresponding to a particular element of an SBRO and/or the particularelement of an SBRO is updated (e.g., based at least in part on the newinformation/data), a confidence rating for the particular element of theSBRO is determined.

The confidence rating is determined based at least in part on variousfeatures of the updated particular element and the information/datacontained therein. For example, the features of the updated particularelement may include the author of the new information/data (e.g.,whether the new data was entered by the patient/subject entity, enteredby a clinician/staff, electronically generated, and/or the like), thesource of the new information/data (e.g., whether the newinformation/data was received directly from the patient/subject entity,directly from a provider, directly from a payor, from a data warehouse,and/or the like), and/or how far removed from the author the source is(e.g., information/data authored by the patient/subject entity andreceived directly from the patient/subject entity vs. data authored bythe patient/subject entity and received via a data warehouse). Thefeatures of the updated particular element may include a status of theinformation/data (e.g., confirmed/verified or not), a status of anaction corresponding to the information/data (e.g., a cancelled orvoided lab work order vs. a lab work order vs. results of completed labwork), the number or percentage of instances of information/datareceived by the ER system 100 and corresponding to the subject entitythat corroborate the new information/data (e.g., the consistency of thenew/information/data with other information/data known about the subjectentity), a ranking of the information/data (e.g., a particular diagnosisis Patient A's primary concern/diagnosis, secondary concern/diagnosis,and/or the like), the level of detail or specificity of information/dataprovided by the new information/data, and/or the like. The features ofthe particular element may include an amount of time elapsed since theoccurrence of an event and/or transaction related to the new data (e.g.,the performance of lab work resulting in the reported lab results, anoffice visit resulting in the reported diagnosis) and when the ER system100 receives the new information/data.

The effect of a particular feature on the confidence rating of theparticular element may depend on the type of the element. For instance,if the type of the element is demographic data (e.g., patient name,address, and/or the like), then the author of the new information/databeing the patient/subject entity may positively affect the confidencerating. However, if the type of the element is a diagnosis or a labresult, then the author of the new information/data being thepatient/subject entity may not positively affect the confidence rating.An algorithm (which may be element type dependent) may be used transformthe information/data regarding the various features of the particularelement and/or new information/data into a numerical confidence ratingfor the particular element. In various embodiments, a confidence scoremay be generated/created and/or determined for a particular element(e.g., a single data field) of an SBRO, a group and/or set of elements(e.g., a group or set of data fields) of an SBRO, and/or for an SBRO asa whole.

In various embodiments, the ER system 100 may provide a user interface(e.g., dashboard, portal 2610, and/or the like) that a user (e.g., aprovider with an appropriate relationship with the subject entity or thepatient/subject entity) may use to access information/data stored withinthe ER corresponding to the subject entity. When the particular element(and/or information/data accessed therefrom) is provided via the userinterface, the confidence rating or an indication of the confidencerating (e.g., high, medium, low) may also be provided via the userinterface.

FIG. 18 provides a flowchart for exemplary process 1800 (e.g.,confidence scoring process 1800) illustrating various processes,procedures, steps, operations, and/or the like for generating andproviding a confidence score for an element of an SBRO, in an exampleembodiment. Starting at step/operation 1802, a request for a confidencescore for an element of an SBRO is received. For example, the SBROprocessing module 265 may generate/create and provide a request forconfidence score for an updated and/or modified element of an SBRO. Forinstance, as part of updating and/or modifying an element of an SBRO,the SBRO processing module 265 may generate/create and provide a requestfor a confidence score. The confidence score engine 235 may then receivethe request for the confidence score. In various embodiments, therequest for the confidence score comprises one or more features of theelement of the SBRO and/or an element type corresponding to the type ofthe element and/or the type of the SBRO. In various embodiments, the oneor more features of the element of the SBRO comprise one or more of (a)author of the data of the element, (b) the source of the data, (c) howfar removed from the author the source, (d) a status of the data, (e) astatus of an action corresponding to the data, (f) the number orpercentage of instances of data received by the ER system 100 andcorresponding to the user that corroborate the data, (g) a ranking ofthe data, (h) the level of detail or specificity of information/dataprovided by the new data, (i) an amount of time elapsed since theoccurrence of an event related to the new data, (j) when the ER system100 received the new data, and/or the like. In an example embodiment, atleast one of the one or more features of the element of the SBRO isselected from the group of (a) author of the data of the element, (b)the source of the data, (c) how far removed from the author the source,(d) a status of the data, (e) a status of an action corresponding to thedata, (f) the number or percentage of instances of data received by theER system 100 and corresponding to the user that corroborate the data,(g) a ranking of the data, (h) the level of detail or specificity ofinformation/data provided by the new data, (i) an amount of time elapsedsince the occurrence of an event related to the new data, and (j) whenthe ER system 100 received the new information/data.

At step/operation 1804, one or more features of the element of the SBROare accessed and/or determined. In an example embodiment, the confidencescore engine 235 receives one or more features of the element of theSBRO as part of the request for the confidence score and extracts thefeatures from the request. In an example embodiment, the confidencescore engine 235 analyzes the element of the SBRO and/or metadatacorresponding thereto to determine one or more features of the elementof the SBRO.

At step/operation 1806, a confidence score algorithm is determined,identified, and/or selected based at least in part on the element typeof the element of the SBRO. For example, each element of an SBRO may beassociated with an element type, based at least in part on the elementtype of the element of the SBRO provided and/or referenced by therequest for the confidence score. The confidence score engine 235 maydetermine, identify, and/or select a confidence score algorithm from aplurality of confidence score algorithms based at least in part on theelement type associated with the element.

At step/operation 1808, the determined, identified, and/or selectedconfidence score algorithm determines a confidence score for the elementof the SBRO. For instance, one or more features of the element may beevaluated using the determined, identified, and/or selected confidencescore algorithm. For example, the determined, identified, and/orselected confidence score algorithm may be executed based at least inpart on at least one of the one or more features of the element todetermine a confidence score for the element. For instance, theconfidence score engine 235 may execute the determined, identified,and/or selected confidence score algorithm to determine a confidencescore for the element.

At step/operation 1810, the confidence score is stored in associationwith the SBRO and/or the element of the SBRO. For example, theconfidence score engine 235 may cause the DSML 245 to update thepersistent storage of the ER data store 211 to store the confidencescore and/or an indication thereof (e.g., high, medium, low, and/or thelike) in association with the element of the SBRO and/or the SBRO. In anexample embodiment, the confidence score engine 235 returns theconfidence score and/or an indication thereof to the SBRO processingmodule 265 and the SBRO process 1600 may cause the DSML 245 to store theconfidence score and/or an indication thereof in association with theelement of the SBRO and/or SBRO in the persistent storage of the ER datastore 211.

In various embodiments, at step/operation 1812, at a later point intime, a request to access and/or read information/data from the SBRO isreceived. For instance, an entity (e.g., the subject entity, providerwith a relationship with the subject entity that enables the provider toview at least some information/data from the ER corresponding to thesubject entity) logs into a user portal 2610 (e.g., via a user computingentity 30) and requests information/data from the ER corresponding tothe subject entity, including the element of the SBRO. The ER system 100may determine and/or confirm that the entity is allowed to access therequested information/data (e.g., via the data access/functioncontroller 255) and access the requested information/data from the ERdata store 211 (e.g., via the extraction processing module 260 and/orthe DSML 245). When the information/data of the element of the SBRO isaccessed, the confidence score corresponding thereto is also accessed.For example, the confidence score and/or an indication thereof may beprovided alongside the information/data of the element of the SBRO. Forinstance, the server 65 may provide the requested information/data andthe confidence score and/or indication thereof such that the usercomputing entity 30 receives the requested information/data and theconfidence score and/or indication thereof. The user computing entitymay then provide the requested information/data and the confidence scoreand/or indication thereof via the user portal 2610 for consumption(e.g., viewing, audible consumption, and/or the like) by the entity.

Various embodiments provide technical improvements by not only providingthe best (e.g., most complete, most reliable, and/or the like)information/data available to the ER system 100 corresponding to anevent and/or transaction, but also providing an indication of howreliable the information/data is and/or how confident the ER system 100is that the information/data provides a true and/or accurate descriptionof the event and/or transaction based at least in part on variousfeatures of the information/data. Thus, various embodiments, provide animproved user experience of accessing information/data from the ERsystem 100 that enables user confidence in the accessedinformation/data.

Data Store Management Layer (DSML)

In one exemplary embodiment, the ER system 100 comprises a DSML 245,also referred to as a Java enabled database interface. The DSML 245comprises a full data object model in the application layer supported bydata objects and relational mapping using object-relational mapping(ORM). ORM allows for the conversion of information/data betweenincompatible type systems using object-oriented programming languages.In one embodiment, the DSML 245 comprises a set of tools to enable Javadata objects to be persisted in potentially disparate databases usingJava-driven modifications, in a manner acceptable by Java. These toolsinclude code parsers, code cleaners, class builders, schema builders,and/or the like.

The DSML 245 solves various technical challenges, including “collectionadd” and “cache flush” problems. In the “collection add,” the ongoingoperation of the ER system 100 requires adding elements to persistentinformation/data collections. Other tools designed to fulfill thefunction of the DSML 245 tend to load such collections entirely intomemory, add elements to them, and re-persist them. As collections growlarger with time, this operation becomes increasingly inefficient. Thus,the DSML 245 allows elements to be added to collections without loadingthe entire collection. The DSML 245 does this by maintaining a“partially loaded” collection where some elements may be loaded whileothers remain only in persistent storage. The DSML 245 also maintains aset of operations performed against the collection while in thispartially loaded state so that the collection as persisted caneventually be brought up to date.

The DSML 245 also maintains a memory cache of recently used dataobjects. This cache can comprise both modified and unmodified dataobjects. At various points in the life-cycle of the cache, it must beflushed to synchronize the persistent storage with it. Other tools,however, can have particularly inefficient caches in that when the cachemust be flushed it is not readily known which data objects have beenmodified and a complex process of discovery must be undertaken todetermined which data objects to flush. As the cache increases in sizeand frequency of flushing, this process can become extremely onerous.This is referred to as the “cache flush” technical problem. The DSML 245is designed to know immediately when a data object is modified via thenatural Java mechanisms and to record this in the cache for easyretrieval so that when the cache must be flushed, the DSML 245 does nothave to engage in a discovery process; rather, the DSML 245 simplyrefers to the known list of modified data objects in the cache.

Part of the DSML 245 comprises a content repository that provides astorage mechanism for ER content items, such as binary large dataobjects and other typically non-data object oriented information/data(e.g., images, documents, rule definitions, message templates,information content, and help files), and may comprise standardizedtechnology (e.g., Java). Content attributes or metadata associated withcontent items may be used for management and selection of discretecontent items. Examples of attributes may include ontology classes,target age, target gender, usage context, effective time, expirationtime, keywords, content publisher/manager (used to distinguish betweensystem-provided content as opposed to content authored by a customer orother provider), status and location. The content repository may beviewed as a generic application information/data “super store” in whichvirtually any type of content can be handled, and that separates contentfrom information/data storage technology. If content is received codedaccording to an external coding system, the content may be applied toappropriate related ontology concepts. Content may be XML exportable andimportable. An API may be used to interact with a content repository,thereby providing technical advantages like the ability to access otherstandard content repositories, allowing external editing, and/ortransporting content between Java content repositories.

In various embodiments, the DSML 245 is an application and/or programlayer that interfaces and/or communicates with other components of theER system 100. For example, the DSML 245 may interface and/orcommunicate with the programmatic reasoning logic 230, the SBROprocessing module 265, the rules engine 250, the data access/functioncontroller 255, the extraction processing module 260, identity matchingservice 240, and/or the like. The DSML 245 also interfaces and/orcommunicates with ER data store 211. For example, the DSML 245 acts asan intermediary between various processing and/or software components ofthe ER system 100 and the ER data store 211. As such, the processingand/or software components of the ER system 100 need not be aware and/orknowledgeable about the ER data store 211. This allows for changes to bemade to the ER data store 211 (e.g., type, database model, organizationthereof, and/or the like) without any changes being required for themessage pre-processing module 220, ingestion processing module 225,programmatic reasoning logic 230, the confidence score engine 235,identity matching service 240, rules engine 250, the dataaccess/function controller 255, the extraction processing module 260,the SBRO processing module 265, and/or other software components of theER system 100. In particular, a new and/or modified DSML 245 may begenerated that is configured to interface and/or communicate with thechanged ER data store 211 without requiring any changes to the ER system100 software components that interface with the (new and/or modified)DSML 245.

In various embodiments, the DSML 245 is specific to a particular 211data store. For example, the DSML 245 may be particular to a databasemodel of the ER data store 211. In various embodiments, the ER datastore 211 may comprise two or more data stores. For example, the ER datastore 211 may comprise a relational database and a key-value database; afirst relational database and a different, second relational database;or SQL database and a key-value database. The ER system 100 may comprisea DSML 245 configured to interface with each of these databases. Forexample, a first DSML 245 may interface and/or communicate with thefirst database of the ER data store 211 and a second DSML 245 mayinterface and/or communicate with the second database of the ER datastore 211.

In various embodiments, the DSML 245 defines a plurality of databaseobjects (DBOs) and is able to map and/or translate between the definedDBOs and primary software objects. In particular, as used herein, themessage pre-processing module 220, ingestion processing module 225,programmatic reasoning logic 230, the confidence score engine 235,identity matching service 240, rules engine 250, the dataaccess/function controller 255, the extraction processing module 260,the SBRO processing module 265, and/or possibly other softwarecomponents of the ER system 100 (e.g., a deferred and/or prioritizedprocessing manager) are considered primary software programs and areconfigured to receive, generate, process, and/or provide primarysoftware objects. In various embodiments, the primary software objectsare Java data objects. For example, the primary software objects may beSBROs and/or components/elements thereof, data transfer objects, and/orthe like.

The primary software programs may pass a primary software object (e.g.,an SBRO or a pointer to an SBRO stored in cache) to the DSML 245 tocause the primary software object to be taken apart and/or decomposedand stored in a manner supported by the corresponding ER data store 211.For example, a primary software object may be taken apart and/ordecomposed into elements that correspond to DBOs. In variousembodiments, the DBOs defined by a DSML 245 are specific to theconfiguration of the durable storage (e.g., ER data store 211). Forexample, the DBOs are classes defined in a way that the DSML 245 canmanage the lifecycle of their instances—they can be any Javaobject/class/data as long as it is defined accordingly. The DSML 245 maybe configured to perform create, read, update, and delete functions(CRUD functions) for DBOs in the ER data store 211.

Once a primary software object has been taken apart and decomposed intoelements that correspond to DBOs, the corresponding DBOs may be writtento the ER data store 211. In various embodiments, writing thecorresponding DBOs to the ER data store 211 comprises determining amapping between of the corresponding DBOs to storage locations withinthe database. For example, if the ER data store 211 is a tabulardatabase, the storage locations within the database may be a row orparticular columns within the row of a particular table where componentsof the DBO should be stored within the database. In various embodiments,the DBOs may be written to the corresponding storage locations withinthe database and/or used to modify existing DBOs written to thecorresponding storage locations as part of a flush and commit operation.

In various embodiments, a DBO is associated with a class. In variousembodiments, a DBO may be associated with an entity class or an embeddedclass. In various embodiments, a DBO associated with an embedded classis (and/or can be) part of another DBO. For example, a DBO associatedwith an embedded class may be part of (e.g., embedded within) a DBOassociated with an entity class. Each entity class is associated with acorresponding persistence manager. In one embodiment, an embedded DBOcannot be accessed directly; rather, embedded BDOs are only accessiblethrough the field in the DBO that references it. An embedded DBO isretrieved from the database when its referencing DBO is retrieved; it isstored in the database when its referencing DBO is stored. That is, theDSML 245 comprises a persistence manager 248 for each entity class anddatabase kind. Each persistence manager 248 is configured to determinethe storage location within the database where a DBO is to be stored (orread from) for a corresponding class. In various embodiments, apersistence manager 248 corresponding to a particular entity class maybe used to reassemble a primary software object from theinformation/data read from a DBO stored in the ER data store 211.

In various embodiments, the ER system 100 comprises a DBO class data.Various DBO classes may be defined and corresponding class definitionsmay be stored in the DBO class data. The DSML 245 may automaticallyinsert automated source code into stored class definitions. For example,the DSML 245 may be configured to recognize a particular pattern to aline of source code within the class definition and based at least inpart on recognizing the particular pattern of the line of source code,identify an insertion point within the class definition. The DSML 245may then insert the automated source code into the class definition atthe insertion point. In various embodiments, a class builder of the DSML245 identifies the insertion points and inserts the automated sourcecode at the insertion points. In various embodiments, the automatedsource code may provide access to an accessor and/or mutator for aclass, for example, via a probe and/or backdoor. For example, the probeinserted into an accessor is configured to detect a request for afield's value. In another example, a probe inserted into a mutatordetects the assignment of a new value to a field. For example, theaccess to a class's accessor and/or mutator methods provided by theautomated source code inserted into a DBO class definition may grant theDSML 245 with access to fields without invoking any of the client'slogic.

Continuing with the above, the mutator's single parameter must have atype and a name that are identical to those of the field being mutated.The DSML 245 requires that the header of a mutator or accessor methodfor a persistent field must be placed on a single source file line. Bythe method's header is meant the source text beginning with themethod's<access> and ending with the right parenthesis terminating themethod's formal parameter list. The behavior of the mutators andaccessors of persistent fields whose multiplicity is collection isdifferent from the usual behavior of mutators and accessors of fields inordinary Java objects. Usually, in an ordinary Java object, if a fieldreferencing a collection has never been initialized, then the field'saccessor will return null. The accessor of a persistent collection fieldin a DBO will never return null: if the field has not been accessed (orset) since its parent DBO was created, the accessor will return an emptycollection. Usually, in an ordinary Java object, when a collectionfield's mutator is invoked, the field is set to the collectionreferenced by the mutator's parameter; if the parameter is null, thefield is set to null. When the mutator of a persistent collection fieldis invoked, any elements in the collection referenced by the field areremoved, and the elements of the collection referenced by the mutator'sparameter are copied to the collection referenced by the field; if theparameter is null, the collection referenced by the field left empty.

In various embodiments, the DSML 245 comprises two layers. For example,in an example embodiment, the DSML 245 comprises a DBO management layer246 and a database interface layer 247. In various embodiments, the DBOmanagement layer 246 manages the DBO class definitions, the insertion ofautomated source code into the DBO class definitions, the translating ofprimary software objects into DBOs, and/or the like. In variousembodiments, the database interface layer 247 is the portion of the ERsystem 100 that interfaces and/or communicates directly with one or moredatabases and/or the like of the persistent ER data store 211. Forexample, the persistence managers 248 operate within the databaseinterface layer 247. Similarly, the DBO management layer 246 isconfigured to interface and/or communicate with the primary softwareprograms of the ER system 100 and the database interface layer 247 isconfigured to interface and/or communicate with the persistent ER datastore 211. In various embodiments, to add a database of a new databasetype to the ER system 100, the database interface layer 247 may beupdated (e.g., a new or modified database interface layer 247 may becreated) and the remainder of the DSML 245 (e.g., the DBO managementlayer 246) and the primary software programs of the ER system 100 neednot be updated and/or modified. In essence, the database interface layer247 is “swappable.” In this manner the DSML 245 enables modificationsand/or changes to the persistent ER data store 211 without requiringsubstantial changes and/or modifications to the ER system 100 as awhole. From the perspective of the primary software programs, theprimary software programs have methods for persisting primary softwareobjects into a database and extracting/retrieving primary softwareobjects from the database, without being aware of how theinformation/data is actually persisted and/or extracted/retrieved.

For example, the DBO management layer 246 presents a databaseabstraction to the primary software objects (e.g., via API calls and/orthe like) and the database interface layer 247 takes that databaseabstraction and persists it to an underlying database. In variousembodiments, the underlying database is a relational database, key valuestore, graph database, and/or other type of the persistent ER data store211. In various embodiments, the database abstraction defines aplurality of DBOs, including entity class objects, entity classcollections, embedded class objects, and embedded class collections. Asnoted above, the embedded class objects are portions of entity classobjects. For example, an example entity class object of class“Individual” may contain the embedded class objects of classes “Name”and “Birthdate.” In turn, the embedded class object of class “Name” maycontain embedded class objects of classes “First Name” and “Last Name.”In various embodiments, the DBO management layer 246 is configured totranslate primary software objects into DBOs that can be provided to thedatabase interface layer 247.

For example, the database interface layer 247 provides a mapping betweenDBOs and specific storage locations within the corresponding database.As noted above the database interface layer 247 is database specific andcorresponds to one database. For example, the storage locationsavailable within the database are specific to the database type andarchitecture. For example, if the corresponding database is tabular, thedatabase interface layer 247 provides a mapping between a DBO and aparticular table and a row of the particular table and/or a particulartable, a row of the particular table, and one or more columns of theparticular table that correspond to a DBO. For example, embedded classobjects may be mapped to particular columns of a particular row of aparticular table and entity class objects may be mapped to a particularrow of a particular table. Continuing with the above example of anentity class object of “Name,” the database interface layer 247 mayidentify an individual table for storing information/data correspondingto individuals known to the ER system 100. For example, an entity classobject of class “Individual” may map to a particular row of theindividual table, and the embedded class object of class “Name” may mapto specific columns of that particular row. In various embodiments, thedatabase interface layer 247 takes the DBOs apart and causes thecorresponding storage locations (e.g., appropriate columns of theappropriate row of the appropriate table in a tabular database) to beread, written to, updated, and/or the like with the information/dataand/or values taken from the DBO.

In various embodiments, the database interface layer 247 takes apartand/or decomposes DBOs. For example, the database interface layer 247 ofthe DSML 245 may receive a DBO and perform serialization of the DBO.Serialization comprises taking the information/data in the DBO (or otherobject) at its memory level and assembling it into a byte string. Thebyte string may then be written directly to the database, for example,because it has no formal structure to it. The serialization form hassome internal structure with the byte string, so that when read back outfrom the database, the parts are identifiable via a deserializationfunction. For example, the deserialization function may use the internalstructure of the byte string to regenerate the corresponding object.

In an example embodiment, the database interface layer 247 interfaceswith the corresponding database through the execution of executablecode. In various embodiments, if the corresponding database supports SQLqueries, the executable code is an SQL statement. In variousembodiments, each entity class object instance is assigned a uniqueidentifier (e.g., a DBO identifier) configured to uniquely identify theentity class object instance within the database and/or globally. Invarious embodiments, the embedded class object instances are assignedunique identifiers (e.g., DBO identifiers) configured to uniquelyidentify the embedded class object instances. In an example embodiment,a DBO identifier (e.g., UUID or GUID) corresponding to an embedded classobject instance may comprise the DBO identifier referencing thecorresponding (e.g., the subsuming) entity class object instance and anindication of the embedded class object to which the embedded classobject instance corresponds. For example, an instance of the embeddedclass object of class “Name” that corresponds and/or is subsumed by theentity class object instance having DBO identifier ABC123, may beassigned the DBO identifier ABC123Name, for example. When the instanceof the entity class object of having DBO identifier ABC123 is to be readfrom the database, the database interface layer 247 may generateexecutable code (e.g., an SQL statement) configured to perform a queryof the database to cause the row (or other database element)corresponding to the DBO identifier ABC123 to be returned. The databaseinterface layer 247 can then use the returned row (or other databaseelement) to rebuild the corresponding DBO(s), which is a primarysoftware object abstracted based at least in part on the databaseabstraction.

In various embodiments, a primary software object may be a relationshipSBRO. The relationship SBRO describes how two entities known to the ERsystem 100 are related (e.g., a patient and the dermatologist that thepatient sees). Each relationship SBRO has two participants (e.g., thepatient and the doctor, the insurance company and the member, the labtechnician and the practice where the lab technician works, and/or thelike) and the role assigned to each participant in the relationship.Thus, the relationship SBRO references both participants. When therelationship SBRO is stored in the persistent ER data store 211, theinformation/data taken and/or extracted from the relationship SBRO isstored in a relationship table (in an example tabular database), and theinformation/data corresponding to the participants is stored in theindividual table (as described in the above example). A row of therelationship table will correspond to the entity class object instanceinto which the relationship SBRO was translated and/or abstracted. Thatrow of the relationship table will include a pointer (e.g., the DBOidentifier) corresponding to entries (e.g., rows) of the individualtable that provide information/data corresponding to the participants ofthe relationship. For example, a first database element of a first tablemay be linked to a different, second database element of a different,second table via the inclusion of the DBO identifier identifying thesecond database element in the first database element. In variousembodiments, when the persistent ER data store 211 comprises a tabulardatabase, the tabular database may store a couple to several hundredtables, each corresponding to different types of primary softwareobjects (e.g., entities/individuals, relationships, health items of afirst type, health items of a second type, observations (e.g., X-rayresults, medical observations), and/or the like).

In various embodiments, the writing and/or updating of DBOs in the ERdata store 211 is associated with a session and a transaction with thesession. In some embodiments, the reading of a DBO in the ER data store211 is also associated with a session and a transaction with thesession. A session identifier and/or a transaction identifier may bestored in association with the metadata corresponding to a DBO that waswritten, updated, and/or read during the session and/or transactionidentified by the session identifier and/or transaction identifier. Invarious embodiments, the transactions are atomic, isolated, and/ordurable.

The DSML 245 may provide a service, Database.flush( ), that operates asa commit( ) that is local to the transaction of the caller. When a“flush” is executed, all DBO modifications made in the currenttransaction are written to the database but are not made durable. Inother words, these modifications are not visible to other DSML 245clients but are visible to the caller of Database.flush( ). The DBOsthat exist in the client's current transaction continue to behave asbefore the command had been called. However, an SQL command explicitlycreated and executed by the client will now see the state of DBOs at thetime of the most recent Database.flush( ) call in the currenttransaction. If there have been no Database.flush( ) calls in thecurrent transaction, then the client will see the state DBOs had whenthey were initially loaded.

The DSML 245 may provide a service, Database.commit( ), that may“commit” the current transaction and start a new transaction. When atransaction is “committed,” all of the remembered modifications are madedurable in the database. Database.commit( ) can cause the accumulatedupdates to be made durable in the database which may include theallocation of a new database record should the transaction include thecreation of an entity class object creation.

The DSML 245 may make an object durable if certain conditions are met.For example, an object becomes durable if it is an entity class objector is reachable from an entity class object, and the transaction inwhich it was created successfully commits. If the object is alreadydurable, a change to the object may be made durable if the transactionin which the change occurred successfully commits.

In an embodiment of DSML 245 a transaction allows the client to create alogical group of permanent object operations. The operations may theneither atomically apply the group to the database or discard the entiregroup. The client may maintain invariants and consistency of the datastored in the database in the face of failures and concurrent activitythrough using transactions.

A transaction may exist in one of two states, open or closed. In oneembodiment an open transaction collects operations to form a logicalgroup of permanent object operations. A closed transaction may notcollect any operations and may not be reopened. When the logical groupof operations is complete or the client determines that a consistentlogical group of operations cannot be made, the client may close thetransaction by respectively committing or aborting the transaction.

In various embodiments, by invoking the commit service the client isindicating that the effects of the operations that occurred during thetransaction result in a valid object state. At this point in certainembodiments the effects of the operations of the transaction are madedurable and operations can no longer take place in the transaction.

FIG. 19 provides a flowchart illustrating various processes, procedures,steps, operations, and/or the like of an example method 1900 forreceiving a CRUD access/function request and completing the requestedCRUD function. For example, the DSML 245 may receive a CRUDaccess/function request, process the request, and cause the requestedCRUD function to be completed. Starting at step/operation 1902, the DSML245 receives the CRUD access/function request. In various embodiments,the CRUD access/function request is generated and provide by a primarysoftware program of the ER system 100 (e.g., message pre-processingmodule 220, ingestion processing module 225, programmatic reasoninglogic 230, the confidence score engine 235, identity matching service240, rules engine 250, the data access/function controller 255, theextraction processing module 260, the SBRO processing module 265, and/orthe like). In an example embodiment, the primary software program of theER system 100 provides the CRUD access/function request via an API callto the DSML 245. As a result, the DSML 245 receives the API call. In anexample embodiment, the API call comprises the primary software objectand/or a pointer to the primary software object in cache. In variousembodiments, the CRUD access/function request corresponds to a primarysoftware object instance and/or a component/element thereof. Forexample, the CRUD access/function request may correspond to a writerequest to write a new SBRO corresponding to a particular subject entity(e.g., identified by a subject entity identifier of the SBRO). Inanother example, the CRUD access/function request may correspond toupdating an SBRO and/or component/element thereof corresponding to aparticular subject entity (e.g., identified by a subject entityidentifier of the SBRO).

At step/operation 1904, one or more DBOs corresponding to the CRUDaccess/function request are identified and/or determined. In particular,one or more DBOs corresponding to the primary software object and/orcomponents/elements thereof are identified and/or determined. Forexample, the DSML 245 may, based at least in part on DBO classdefinitions stored in the DBO class data, identify and/or determine DBOscorresponding to the primary software object and/or components/elementsthereof. For example, the identified and/or determined DBOs may beassociated with one or more entity classes and one or more embeddedclasses. For instance, the primary software object may be translatedfrom a primary software object into DBOs and/or data elements that maybe stored in the ER data store 211. For example, a Java object cannot bepersisted in a relational database as a fully functional Java object.Rather, the DSML 245 takes apart and/or decomposes the Java object intocorresponding DBOs that may be persisted in the ER data store 211. TheDSML 245 may further be configured to later put back together and/orcombine DBOs to reform a fully functional Java object, for instance.

At step/operation 1906, the identified and/or determined DBOscorresponding to the primary software object instance are mapped tostorage locations within the ER data store 211 (and/or a database of theER data store 211). For instance, in an example embodiment, the ER datastore 211 comprises (or is) a tabular database, and the storage locationcorresponding to a DBO indicates which table the DBO should be storedin, which row of the table the DBO should be stored in, which columns ofwhich rows of the table the DBO should be stored in, and/or the like. Inan example embodiment, the DSML 245 may call a persistence manager 248corresponding to the entity class of the DBO to determine the storagelocation within the ER data store 211 for the DBO. As noted previously,the persistence manager 248 is particular to an entity class. Forexample, the entity class of an identified and/or determined DBO may bedetermined and used to identify and/or determine the appropriatepersistence manager 248 for mapping the DBOs to storage locations withinthe ER data store 211.

At step/operation 1908, the DSML 245 uses a connect method to connectwith the ER data store 211 (e.g., database). For example, the DSML 245may initiate a session with a database, open a transaction, and/or thelike. In an example embodiment, the DSML 245 may confirm that a sessionis open and a transaction is open. In certain embodiments,step/operation 1906 may be performed after step/operation 1908.

At step/operation 1910, the DSML 245 invokes and executes the processesrequired for completing the requested CRUD function. For example, a loadprocess may be invoked and executed if the requested CRUD function is aread function. In another example, an update process may be invoked andexecuted if the requested CRUD function is an update function. Inanother example, an insert process may be invoked and executed if therequest CRUD function is a write function. FIGS. 20-23 provideflowcharts illustrating the invoking and executing processes by the DSML245 to cause the completion of exemplary CRUD functions.

Continuing with FIG. 19, at step/operation 1912, the DSML 245 executes adisconnect method to disconnect from the data store 211 (e.g.,database). For example, the DSML 245 may close a session and/or atransaction of a session.

Various examples of the DSML 245 performing example CRUD functions willnow be described with reference to FIGS. 20-23.

FIG. 20 provides a flowchart illustrating various processes, procedures,steps, operations, and/or the like of an example method 2000 forexplicit loading of primary software object. As referred to herein,explicit loading corresponds to a read function where the uniqueidentifier for a DBO instance corresponding to the primary softwareobject is known (e.g., included in the request). In various embodiments,the processes, procedures, steps, operations, and/or the likeillustrated in FIG. 20 are executed between the execution of the connectmethod at step/operation 1908 and the execution of the disconnect methodat step/operation 1912.

Starting at step/operation 2002, the DSML 245 may determine that theCRUD access/function request is a read request corresponding to a DBOinstance and that the CRUD access/function request comprises a uniqueidentifier identifying the DBO instance. For example, the DSML 245 mayprocess the CRUD access/function request, and determine, based at leastin part on the processing, that the CRUD access/function request is aread request corresponding to a DBO instance and that it comprises theunique identifier identifying the DBO instance.

At step/operation 2004, the DSML 245 determines whether the DBO instanceidentified by the unique identifier is currently loaded into cache as aprimary software object. For example, the DSML 245 may query the cacheusing the unique DBO identifier identifying the DBO instance todetermine if the DBO is currently loaded into cache as a primarysoftware object (and/or as a part of a primary software object).

When it is determined, at step/operation 2004, that the DBO instance iscurrently loaded into cache, the process continues to step/operation2006. At step/operation 2006, the DSML 245 returns a reference and/orpointer to the loaded primary software instance in the cache. Forexample, the DSML 245 may provide a response to the primary softwareprogram that submitted the CRUD access/function request that includes areference and/or a pointer to the loaded primary software instance inthe cache.

When it is determined, at step/operation 2004, that the DBO instance isnot currently loaded into cache, the process continues to step/operation2008. At step/operation 2008, the DSML 245 executes a load method on theappropriate 211 data store. Execution of the load method on theappropriate data store (e.g., the database where the DBO instanceidentified by the unique identifier is stored) includes passing theclass of the DBO instance and the unique identifier identifying the DBOinstance to the load method corresponding to the appropriate 211 datastore.

At step/operation 2010, a first persistence manager 248 instancecorresponding to the class of the DBO is identified. For example, theDSML 245 may look up the first persistence manager 248 instance thatcorresponds to the class of the DBO and invoke the load method on thatfirst persistence manager 248 instance. At step/operations 2012, thefirst persistence manager 248 obtains executable code (e.g., an SQLstatement) for loading instances of DBOs of the class corresponding tothe DBO. For example, the executable code (e.g., SQL statement) may bethe value of the “LOAD CODE” (e.g., “LOAD SQL”) constant in the firstpersistence manager 248 instance. At step/operation 2014, the firstpersistence manager 248 binds the unique identifier identifying the DBOinstance to the query and/or “?” parameter of the executable code (e.g.,SQL statement). In various embodiments, the executable code (e.g., SQLstatement) only includes one query and/or “?” parameter. The firstpersistence manager 248 may then execute and/or cause the execution ofthe executable code (e.g., SQL statement).

At step/operation 2016, the execution of the executable code (e.g., SQLstatement) causes a corresponding element from the data store 211 (e.g.,database) to be returned. For example, a data store elementcorresponding to the DBO instance may be returned. In an exampleembodiment, the database may be a tabular database and the data storeelement may be a row of a table corresponding to the DBO instance orselect fields from a specific row. The returned data store element isfed into a second persistence manager 248 instance generated by theclass builder. For example, the class builder may generate or call asecond persistence manager 248 instance corresponding to the class ofthe DBO instance, and the returned data store element may be fed intoand/or provided as input to the second persistence manager 248 instance.

At step/operation 2018, the returned data store element is transformedinto a fully functional primary software object by the secondpersistence manager 248 instance, such as an SBRO or a portion of anSBRO. For example, the second persistence manager 248 instance mayperform a deserialization function to regenerate and/or rebuild theprimary software object. For example, one or more fields of a primarysoftware object may be initialized and/or populated based at least inpart on the returned data store element, the open session, opentransaction, and/or the like. In an example embodiment, a constructmethod is used to transform the returned data store element into a fullyfunctional primary software object and/or to construct a fullyfunctional primary software object based at least in part on thereturned data store element. At step/operation 2020, the fullyfunctional primary software object is loaded into cache. For example,the DSML 245 may cause the fully functional primary software objectconstructed based at least in part on the returned data store elementinto cache. The process may then continue to step/operation 2006, wherethe DSML 245 returns a reference and/or pointer to the loaded primarysoftware instance in the cache. For example, the DSML 245 may provide aresponse to the primary software program that submitted the CRUDaccess/function request that includes a reference and/or a pointer tothe loaded primary software instance in the cache.

FIG. 21 provides a flowchart illustrating various processes, procedures,steps, operations, and/or the like of example method 2100 for implicitloading of a primary software object. As referred to herein, implicitloading corresponds to a read function where the request is generatedvia an accessor method based at least in part on a reference to aprimary software object instance from some already loaded primarysoftware object instance. For example, a probe in the accessor methodmay detect the request and load the desired primary software objectinstance if it is not already loaded (e.g., into cache). A referenceand/or pointer to the desired primary software object instance (e.g., inthe cache) is then provided to the requesting primary software program.In various embodiments, the processes, procedures, steps, operations,and/or the like illustrated in FIG. 21 are executed between theexecution of the connect method at step/operation 1908 and the executionof the disconnect method at step/operation 1912.

Starting at step/operation 2102, the DSML 245 may determine that theCRUD access/function request has been received (e.g., via an accessormethod) and is a read request corresponding to a primary software objectinstance and that the CRUD access/function request does not comprises aunique identifier identifying the corresponding DBO instance. Forexample, the DSML 245 may process the CRUD access/function request, anddetermine, based at least in part on the processing, that the CRUDaccess/function request is a read request corresponding to a DBOinstance (e.g., that corresponds to the primary software object) thatdoes not comprise the unique identifier identifying the DBO instance.

At step/operation 2104, a get function is executed to determine and/oridentify a DBO instance identifier (e.g., a unique identifieridentifying a DBO instance). In an example embodiment, the get functionis executed using a corresponding entity proxy. For example, when anobject is loaded from the data store 211 that references other DBOand/or primary software object instances, the DSML 245 may create and/orgenerate a corresponding entity proxy and place it in a special field inthe referencing object. The entity proxy contains the information/datanecessary to load the referenced other DBO and/or primary softwareobject instance. For example, the entity proxy may include and/or haveaccess to a DBO identifier and/or other unique identifier configured toidentify the referenced other DBO and/or primary software objectinstance. Thus, the get function may cause the entity proxy to providethe unique identifier corresponding to the referenced DBO and/or primarysoftware object instance for which the read function was requested.

At step/operation 2106, the DSML 245 determines whether the DBO instanceidentified by the unique identifier is currently loaded into cache as aprimary software object. For example, the DSML 245 may query the cacheusing the unique DBO identifier identifying the DBO instance todetermine if the DBO is currently loaded into cache as a primarysoftware object (and/or as a part of a primary software object).

When it is determined, at step/operation 2106, that the DBO instance iscurrently loaded into cache, the process continues to step/operation2108. At step/operation 2108, the DSML 245 returns a reference and/orpointer to the loaded primary software instance in the cache. Forexample, the DSML 245 may provide a response to the primary softwareprogram that submitted the CRUD access/function request that includes areference and/or a pointer to the loaded primary software instance inthe cache.

When it is determined, at step/operation 2106, that the DBO instance isnot currently loaded into cache, the process continues to step/operation2110. At step/operation 2110, the DSML 245 executes a load method on theappropriate data store 211. Execution of the load method on theappropriate data store (e.g., the table or database where the DBOinstance identified by the unique identifier is stored) includes passingthe class of the DBO instance and the unique identifier identifying theDBO instance to the load method corresponding to the appropriate 211data store.

At step/operation 2112, a first persistence manager 248 instancecorresponding to the class of the DBO is identified. For example, theDSML 245 may look up the first persistence manager 248 instance thatcorresponds to the class of the DBO and invoke the load method on thatfirst persistence manager 248 instance. At step/operations 2114, thefirst persistence manager 248 obtains executable code (e.g., an SQLstatement) for loading instances of DBOs of the class corresponding tothe DBO. For example, the executable code (e.g., SQL statement) may bethe value of the “LOAD CODE” (e.g., “LOAD SQL”) constant in the firstpersistence manager 248 instance. At step/operation 2116, the firstpersistence manager 248 binds the unique identifier identifying the DBOinstance to the query and/or “?” parameter of the executable code (e.g.,SQL statement). In various embodiments, the executable code (e.g., SQLstatement) only includes one query and/or “?” parameter. The firstpersistence manager 248 may then execute and/or cause the execution ofthe executable code (e.g., SQL statement).

At step/operation 2118, the execution of the executable code (e.g., SQLstatement) causes a data store element to be returned. For example, adata store element corresponding to the DBO instance may be returned. Inan example embodiment, the database may be a tabular database and thedata store element may be a row of a table corresponding to the DBOinstance or select fields from a specific row. The returned data storeelement is fed into a second persistence manager 248 instance generatedby the class builder. For example, the class builder may generate orcall a second persistence manager 248 instance corresponding to theclass of the DBO instance and the returned data store element may be fedinto and/or provided as input to the second persistence manager 248instance.

At step/operation 2120, the returned data store element is transformedinto a fully functional primary software object by the secondpersistence manager 248 instance, such as an SBRO or a portion of anSBRO. For example, the second persistence manager 248 instance mayperform a deserialization function to regenerate and/or rebuild theprimary software object. For instance, one or more fields of a primarysoftware object may be initialized and/or populated based at least inpart on the returned data store element, the open session, opentransaction, and/or the like. In an example embodiment, a constructmethod is used to transform the returned data store element into a fullyfunctional primary software object and/or to construct a fullyfunctional primary software object based at least in part on thereturned data store element. At step/operation 2122, the fullyfunctional primary software object is loaded into cache. For example,the DSML 245 may cause the fully functional primary software objectconstructed based at least in part on the returned data store elementinto cache. The process may then continue to step/operation 2006, wherethe DSML 245 returns a reference and/or pointer to the loaded primarysoftware instance in the cache. For example, the DSML 245 may provide aresponse to the primary software program that submitted the CRUDaccess/function request that includes a reference and/or a pointer tothe loaded primary software instance in the cache.

FIG. 22 provides a flowchart illustrating various processes, procedures,steps, operations, and/or the like of an example method 2200 for writinga primary software object instance to persistent data store (e.g., ERdata store 211) and/or updating information/data stored in persistentdata store corresponding to primary software object instance. Startingat step/operation 2202, the DSML 245 may determine that the CRUDaccess/function request has been received (e.g., via an accessor method)and is a write and/or update request corresponding to a primary softwareobject. The primary software object has been translated and/orabstracted into one or more DBOs (e.g., entity class object instancesand embedded class(es) object instances).

At step/operation 2, the DSML 245 causes persistence hierarchy classesfields declared for the capturing of the DBOs corresponding to theprimary software object instance. For example, the persistence hierarchyclasses may include a persistent flag and a modified flag. In variousembodiments, a persistent flag indicates whether or not a DBO hasalready been stored to the persistent ER data store 211. For example, ifthe persistent flag is set to true, then the DBO has been previouslystored in the persistent ER data store 211 and if the persistent flag isset to false, then the DBO has not been previously stored in thepersistent ER data store 211. In various embodiments, the modified flagindicates whether the DBO has been modified or not since the DBO waswritten to the persistent ER data store 211. In an example embodiment,the modified flag indicates whether or not the persistent ER data store211 should be updated (e.g., to include/insert a new DBO or to update anexisting DBO) based at least in part on the corresponding DBO. Forexample, if the modified flag is set to true, then the persistent ERdata store 211 should be updated and/or synchronized with the DBO;however, if the modified flag is set to false, then the persistent ERdata store 211 need not be synchronized with the DBO because the DBO hasnot been modified. In an example embodiment, the persistence hierarchyclasses may further include a DBO identifier and a set of modifiedcollections. Similar to the modified flag, the set of modifiedcollections contains references to child collection proxies that havebeen modified since the last synchronization. Similar to the entityproxies described above, collection proxies are references and/orpointers stored in one DBO instance (and/or a database elementcorresponding to the DBO) that point to or reference another DBOinstance and/or a collection.

In an example embodiment, the persistent flag for a newly created entityclass object instance that has not yet been stored in the persistent ERdata store 211 is initially not set (and/or set to False) since theentity class object instance has not yet been written in the persistentER data store 211. However, the new entity class object instance shouldbe written to the persistent ER data store 211 as the nextsynchronization point, so the modified flag corresponding to the newentity class object instance is set (e.g., set to True). In an exampleembodiment, a newly created entity class object instance has no modifiedcollections.

At step/operation 2206, the DSML 245 executes and/or causes execution ofthe persistent hierarchy classes logic. In various embodiments,execution of the persistent hierarchy classes logic causes thecorresponding DBO instance to be added to the transaction cache (e.g.,the cache corresponding to the open transaction). For example, aconstructor may invoke a method on the current write transaction thatcauses the DBO instance to be added to the corresponding transactioncache. In an example embodiment, the transaction cache is used by theDSML 245 as a log of DBO instances that the DSML 245 must manage and/ortake care of.

At step/operation 2208, the DSML 245 initiates a synchronization of thedurable storage with the current state of the DBO instances stored inthe transaction cache. For example, the DSML 245 may cause a flushand/or commit method to be invoked. In various embodiments, the flushand/or commit method is invoked responsive to the transaction cachereaching a certain level of fullness (e.g., at least 80-99% full),responsive to a periodic synchronization between the persistent ER datastore 211 and the ER system 100 (e.g., a flush and/or commit method isinvoked every ten seconds, every thirty seconds, every minute, everyfive minutes, every ten minutes, every half an hour, every hour, and/orthe like), and/or responsive to other criteria being satisfied.

At step/operation 2210, for each DBO instance in the transaction cache,it is determined whether the modified flag is set (and/or set to True).If the modified flag is not set (and/or set to False) for a DBOinstance, the process for that DBO instance is completed as there is noneed to synchronize the persistent ER data store 211 with that DBOinstance. For example, the DSML 245 may determine whether the modifiedflag is set (and/or set to True) for each DBO instance in thetransaction cache. For the DBO instances having the modified flag set(and/or set to True), the process continues to step/operation 2212. Atstep/operation 2212, it is determined whether the persistent flag is set(and/or set to True). For example, if the persistent flag correspondingto a DBO instance is set (and/or set to True), the DBO instance has beenpreviously written to the persistent ER data store 211 and an updatefunction should be performed. In such a case the process continues tostep/operation 2216. In another example, if the persistent flagcorresponding to a DBO instance is not set (and/or set to False), theDBO instance has not been previously written to the persistent ER datastore 211 and an insert function should be performed. In such a case theprocess continues to step/operation 2214.

At step/operation 2214, an insert function is executed and a newdatabase element corresponding to the DBO instance is inserted into thepersistent ER data store 211 is updated by the DSML 245. For example, aninsert function may be executed to cause a new database element to beinserted into the persistent ER data store 211. The new database elementmay include values extracted and/or taken from (e.g., via aserialization function) the DBO instance. In an example embodimentwherein the persistent ER data store 211 comprises a tabular database, anew row may be inserted into the appropriate table of the database andone or more columns of the new row may be populated based at least inpart on values extracted and/or taken from the DBO instance. Afterinserting the new database element into the persistent data store, thepersistent flag for the DBO instance is set (and/or set to True) and themodified flag is unset (and/or set to False).

At step/operation 2216, and update function is executed and the existingdatabase element corresponding to the DBO instance and stored in thepersistent ER data store 211 is updated by the DSML 245. For example,values extracted and/or taken from (e.g., via a serialization function)the DBO instance are used to update the existing database element,and/or portions thereof, corresponding to the DBO instance. For example,the modified values extracted and/or taken from the DBO instance (e.g.,via a serialization function) may be written to the existing databaseelement and/or portion thereof corresponding to the DBO instance. Aftersynchronizing the existing database element with the DBO instance in thetransaction cache, the modified flag of the DBO instance is unset(and/or set to False).

At step/operation 2218, for each collection proxy in the DBO instance'smodified collection set, the set of modified collections is cleared. Forexample, for each collection proxy in the DBO instance's modifiedcollection set, a flush method may be invoked to synchronize the durableER data store 211 with the current state of the collection. In anexample embodiment, the synchronization is performed by obtaining and/orgenerating an SQL statement (or other executable code) suitable foradding an element of the collection to the appropriate table of thedatabase (e.g., for a tabled-based database) and executing the SQLstatement (or other executable code). Once the synchronization has beenperformed for each collection proxy of the DBO instance's modifiedcollection set, the modified collection set is cleared.

At step/operation 2220, the oldest write transaction open at the pointwhen the synchronization was invoked is committed. For example, aftereach inserting and/or updating database elements corresponding to theDBO instances having set (and/or set to True) modified flags, the oldestwrite transaction open at the point the synchronization was invoked iscommitted and/or closed. After the write transaction is committed, anynew database elements that were inserted and anychanges/updates/modifications made to existing database elements will bevisible to other activities (e.g., CRUD functions) interacting with thepersistent ER data store 211.

FIG. 23 provides a flowchart illustrating various processes, procedures,steps, operations, and/or the like of an example method 2300 forperforming an insert or update function by the DSML 245. In variousembodiments, the method 2300 corresponds to the processes, procedures,steps, operations, and/or the like performed during steps/operations2214 and/or 2216. Starting at step/operation 2302, an insert or updatefunction is invoked for a DBO instance. For example, the DSML 245 mayinvoke an insert or update function on a DBO instance.

At step/operation 2304, the DSML 245 identifies and/or determines theappropriate persistence manager 248 for the DBO instance on which theinsert or update function is being invoked. For example, a persistencemanager 248 instance corresponding to the class of the DBO instance isidentified. For example, the DSML 245 may look up the persistencemanager 248 instance that corresponds to the class of the DBO instance.

At step/operation 2306, the DBO instance is provided as input to theidentified persistence manager 248 instance. For example, the DBOinstance may be fed into the identified persistence manager 248instance. In various embodiments, the persistence manager 248 instanceis configured to take apart and/or decompose the DBO instance via aserialization function. In various embodiments, the persistence manager248 is configured to generate executable code (e.g., an SQL statement)that, when executed causes the insert and/or update function to becompleted. To determine whether an insert or update function is to becompleted, at step/operation 2308, it is determined whether the DBOinstance is a new instance (e.g., not yet written to the persistent ERdata store 211). This determination may be made (e.g., by thepersistence manager) based at least in part on the persistent flag ofthe DBO instance, as described above.

When it is determined at step/operation 2308 that the DBO instance isnot a new DBO instance (e.g., that the DBO instance has already beenwritten to the persistent ER data store 211), for example, based atleast in part on the persistent flag of the DBO instance being not set(and/or set to False)), the process continues to step/operations 2310.At step/operation 2310, the persistence manager 248 instance producesand/or generates executable code (e.g., an SQL statement) to cause adatabase element corresponding to the DBO instance to be updated basedat least in part on the modifications to the DBO instance since the DBOinstance was last synchronized with the persistent ER data store 211.For example, the persistence manager 248 instance may produce and/orgenerate executable code (e.g., an SQL statement) configured to, whenexecuted, cause the persistent ER data store 211 to be updated so as tobe synchronized with the DBO instance. In an example embodiment, theexecutable code (e.g., SQL statement) only causes database elementportions that correspond to portions of the DBO instance (e.g., embeddedclass object instances) that have been updated and/or modified since thepersistent ER data store 211 was last synchronized with the DBO instanceto be updated and/or modified. For example, in an example embodimentwherein the persistent ER data store 211 comprises a tabular database,the executable code (e.g., SQL statement) is configured to cause, whenexecuted, one or more columns of a particular row of a particular tablecorresponding to the modified DBO instance to be updated.

When it is determined at step/operation 2308 that the DBO instance is anew DBO instance that has not yet been written to the persistent ER datastore 211 (e.g., the persistent flag of the DBO instance is not set(and/or set to False)), the process continues to step/operations 2312.At step/operation 2312, the persistence manager 248 instance producesand/or generates executable code (e.g., an SQL statement) to cause adatabase element corresponding to the DBO instance to insert a newdatabase element corresponding to the DBO instance into the persistentER data store 211. For example, the persistence manager 248 instance mayproduce and/or generate executable code (e.g., an SQL statement)configured to, when executed, cause a database element to be insertedinto the persistent ER data store 211 and populated with values takenand/or extracted from (e.g., via a serialization function) the DBOinstance. For example, in an example embodiment wherein the persistentER data store 211 comprises a tabular database, the executable code(e.g., SQL statement) is configured to cause, when executed, a row to beinserted into a particular table and one or more columns of the insertedrow of the particular table to be populated with values taken and/orextracted from the DBO instance.

At step/operation 2314, the executable code (e.g., SQL statement)produced and/or generated by the persistence manager 248 instance atstep/operation 2310 or 2312 is executed to cause the persistent ER datastore 211 to be synchronized with the DBO instance. For example, theDSML 245 may cause the execution of the executable code (e.g., SQLstatement) generated by the persistence manager 248 instance to beexecuted (e.g., by the processing element 205 of the server 65). Forexample, the execution of the executable code (e.g., SQL statement) maycause the insertion of a database element into the persistent ER datastore 211 and the population of at least a portion of the inserteddatabase element (alternatively, the database element may be inserted inan already populated manner, in an example embodiment). In anotherexample, the execution of the executable code (e.g., SQL statement) maycause the updating and/or modifying of at least a portion of a databaseelement stored in the persistent ER data store 211. In an exampleembodiment, after completion of step/operation 2314, the process maycontinue to step/operation 2218 of FIG. 22 and/or may be performediteratively for various DBO instances to which the persistent ER datastore 211 is to be synchronized.

Additionally, various embodiments of the DSML 245 support amulti-threaded and multi-process runtime environment. To accomplishthis, the DSML 245 may employ concurrency control features includinglocks, advisory locks, and versioned objects.

As noted, various embodiments of the DSML 245 an entity class instancecan be locked by passing it to Database#lock( ). This method may lockthe object in durable storage for the duration of the currenttransaction. This lock has the same effect as updating the row indurable storage and will be held until the end of the transaction.

Further, in various embodiments of the DSML 245, an advisory lock may beimplemented. An advisory lock is effectively a lock on a name meaningfulto the application programmer. An advisory lock may be acquired bypassing a name, which is a string, to Database#lockAdvisory( ). Advisorylocks are held until the end of the transaction and areexclusive—meaning no two transactions may hold the same advisory lock atthe same time. If a thread attempts to acquire an advisory lockcurrently held by some other transaction, the attempt will be blockeduntil the transaction holding the lock terminates. Advisory locks areimplemented on top of durable storage mechanisms and thus may spanthreads and processes.

In various embodiment of the DSML 245, the DSML 245 has the ability toimplement for versioned objects. Versioned objects, also known asoptimistic locking, may be enabled for any entity class. For example,when utilizing versioned objects, the DSML 245 may prevent versionconflicts. A version conflict occurs when two activities attempt tomodify an instance that was concurrently read from two differentactivities. When any other conflicting activity attempts to commit itschanges, the DSML 245 may throw an exception causing the activity toabort and retry. These versioned objects may be enabled through theinclusion of several pieces of state in the entity class instances. Whenan entity class instance is loaded from durable storage, the persistentversion and version fields are both initialized to the instance'sversion number from durable storage. When a modification is made to somepart of a versioned instance's persistent object tree, if the instance'smodified flag is not set, the modification mechanism will increment theversion field by one and the instance's modified flag is set (and/or setto True). When it is time to write the modified instance to durablestorage, the DSML 245 will check that the version number in durablestorage matches the value of the persistent version field. If these donot match, an exception may be thrown, and the transaction is aborted.If they do match, DSML 245 may write the object to durable storage usingthe outlined persistence manager 248 mechanisms. The version number indurable storage is then set to the instance's incremented version field,and DSML 245 sets the instances persistent version field to be the sameas the version field.

As will be recognized, various embodiments of the DSML 245 solvetechnical problems corresponding to an application or programinterfacing with a persistent 211 data store. In particular,applications and/or programs that store information/data in a databasetend to be aware of the database and its structure that theinformation/data is being stored in. As such, if the database being usedby the application and/or program is to be changed, large portions ofthe application and/or program have to be re-engineered and rebuilt.However, various embodiments of the DSML 245 provide technical solutionsto the technical problem of switching which database is being used tostore information/data by an application and/or program. For example,the primary software programs of the ER system 100 are not aware of theunderlying structure of the persistent ER data store 211 and thestructure and/or type of the persistent ER data store 211 may be changedwithout any changes being made to the primary software programs of theER system 100. For example, the primary software programs of the ERsystem 100 are database agnostic. In particular, the DSML 245 providesthe primary software programs of the ER system 100 with a databaseabstraction (e.g., a database agnostic model of a database) with whichthe primary software programs may interface while the database interfacelayer 247 of the DSML 245 provides a mapping between the databaseabstraction and the actual structure of the database. Thus, ER system100 may switch to use of a different structure and/or type of databasewith only required changes being to the database interface layer 247 ofthe DSML 245. For example, only the mapping between the databaseabstraction and the actual structure of the database need be updated fora different database to be used. Moreover, the DSML 245 enables the ERsystem 100 to use multiple databases (e.g., various information/data maybe stored in the most appropriate database type for that particularinformation/data) through the use of multiple DSMLs 245 and/or multipledatabase interface layers 247. Thus, various embodiments of the DSML 245provide technical improvements to the field of application and/orprogram interactions with a database and/or other persistent 211 datastore.

Rules Engine

In various embodiments, the ER system 100 uses a rules engine 250 toexecute XML-based rules. In one embodiment, the rules engine 250evaluates ERs and or SBROs to initiate “actions.” Each rule may be arule that (a) is identifiable by a rules identifier (e.g., via auniversally unique identifier (UUID) or a globally unique identifier(GUID)), and (b) represents a condition to be tested with actions to betaken depending on the result (e.g., an action to be taken if thecondition is satisfied and/or an action to be taken if the condition isnot satisfied). Generally, rules are used to make inferences aboutadditional ER states to be created. In one embodiment, rules areexpressed as XML, documents and comprise a rule “predicate” and one ormore rule “actions.” The predicate comprises a “selection” and a“condition.”

The separation of the rule predicate into <selection/> and <condition/>should be thought of as selecting a population of actors via<selection/> upon which the rule <condition/> is to be tested. Forexample, the <selection/> determines whether the actor upon which therule is currently operating is a member of the population upon which the<condition/> will be tested and the <actions/> will be executed. Thishas the technical benefit of allowing the rule to be written with<conditionFalse/> actions without always executing actions whenever therules are tested because <selection/> also serves as a gate keeper tothe <condition/> evaluation. FIG. 25A illustrates an example basicstructure of a rule.

In one embodiment, the rules engine 250 flow of control evaluates<selection/>. If <selection/> is true, the rules engine 250 thenevaluates <condition/>. If <condition/> is true, the rules engine 250then executes actions specified by <conditionTrue/> or <action/>. Ifnot, then the rules engine 250 executes actions specified by<conditionFalse/>. FIG. 25B illustrates some example Boolean operatorsthat may be included in the conditions section of a rule, in accordancewith an example embodiment.

As will be recognized, <selection/> and <condition/> have the same form;the difference between them is in their usage. For instance,<selection/> and <condition/> are single expressions yielding a Booleanvalue. These expressions may be either simple—involving a single<conditionPhrase/> or negation of a <conditionPhrase/>. Or theconditions may be compound involving <and/> and <or/> with enclosed tagsof those same types. FIG. 25C illustrates an example structure and somepossible operators that may be included in the <conditionPhrase/>portion of a rule.

In one embodiment, a condition phrase specifies a Boolean expression tobe evaluated and may impose count, time, and order restrictions on thepossible candidates to be examined.

In one embodiment, a condition clause specifies the elements forevaluating a specific rule expression: rule condition, operator, andvalue. The condition clause is an expression where the conditionsupplies a value that is compared to some constant value by thespecified operator, as illustrated in FIG. 25D.

In one embodiment, an occurrence count specifies the specific number ofoccurrences of a match satisfying the rule condition, operator, andvalue which must be present for the condition phrase to be true. Anexample occurrence count is illustrated in FIG. 25E.

In one embodiment, a range occurrence count specifies the specific rangeof the number of occurrences of a match satisfying the rule condition,operator, and value which must be present for the condition phrase to betrue. An example range occurrence count condition is illustrated in FIG.25F.

In one embodiment, a period time interval specifies the time after whicha match satisfying the rule condition, operator, and value must haveoccurred. An example of a period time interval condition is illustratedin FIG. 25G.

In one embodiment, a range time interval specifies the range of timeduring which a match satisfying the rule condition, operator, and valuemust have occurred. An example range time interval condition isillustrated in FIG. 25H.

In one embodiment, a rule condition is responsible for extracting ERinformation/data to be compared against constant value(s) using theoperator for determining truth, an example format of which isillustrated in FIG. 25I. For instance, the DSML 245 may be used toaccess information/data from an ER such that the rule may be evaluatedand/or applied to an ER.

In one embodiment, condition values are constants which may be used bythe rule condition as parameters for extracting the appropriate ERinformation/data. FIG. 25J illustrates an example format for defining acondition value, in an example embodiment.

In one embodiment, a collection of rule condition values are values tobe used by the rule condition. FIG. 25K illustrates an example formatfor defining a rule condition value, in an example embodiment.

In one embodiment, the operator compares the values extracted by therule condition against the constant values specified by the rule todetermine truth. FIG. 25L illustrates an example format for defining anoperator of a rule condition.

In one embodiment, the clause type controls what fields are displayedfor particular condition clauses on an eForm for defining rules and torestrict the acceptable selection values. FIG. 25M illustrates anexample format for defining a clause type of a condition, in an exampleembodiment.

In one embodiment, the target encapsulates the value in various forms tobe compared by the operator against the information/data extracted bythe rule condition. FIG. 25N illustrates an example format of a targetof a rule condition, in accordance with an example embodiment.

In one embodiment, an amount is a value combined with a unit for thoserules which allow these. FIG. 25O illustrates an example format of anamount of a rule condition, in accordance with an example embodiment.

In one embodiment, a quantity is a value type combining a value with aunit. FIG. 25P illustrates an example format for defining a quantity ofa rule condition, in accordance with an example embodiment.

In one embodiment, values are used for computation within the rulesengine 250. In the rule language, values are untyped and converted toappropriate types at runtime based at least in part on usage. FIG. 25Qillustrates an example format for defining a value of a rule condition,in accordance with an example embodiment.

In one embodiment, a unit is a concept that represents a measurementunit. FIG. 25R illustrates an example format for defining a unit of arule condition, in accordance with an example embodiment.

In one embodiment, a rule action identifier maps to an OPDO to beexecuted (e.g., provided to the ingestion processing module 225). Theset of available OPDOs may be stored within the ER system 100 and beidentifiable by one or more OPDO identifiers, such as UUIDs or GUIDs.Thus, each OPDO is referenced using its unique identifier. In suchcases, the OPDO may have been partially populated prior to executing thecorresponding action. Thus, when executed, any remaining portions of theOPDO are automatically populated and provided for execution. FIG. 25Sillustrates an example format for designating a rule action, inaccordance with an example embodiment.

In one embodiment, actions are the set of <action/> elements that are tobe executed upon evaluation of the rule condition. If the rule conditionevaluates to true, then the set of <action/> elements contained within<conditionTrue/> are executed. If the rule conditions evaluate to false,then the set of <action/> elements contained within <conditionFalse/>are executed. FIG. 25T illustrates an example format for defining anaction of a rule, in accordance with an example embodiment.

In one embodiment, <and/> is used to aggregate <conditionPhrase/> and<conditionClause/> such the result of the <and/> is true if-and-only-ifall of the sub-elements are true. Sub-elements may include additionallevels of <and/> and <or/>. Similarly, <or/> is used to aggregate<conditionPhrase/> and <conditionClause/> such the result of the <or/>is true if any of the sub-elements are true. Sub-elements may includeadditional levels of <or/> and <and/>.

In one embodiment, the rule context specifies the information/data uponwhich the rule operates including the owning actor, and in the case ofdynamic rule running, the specific object which has been modified. Inthe case of dynamic rule running, including the modified data objectallows the rule to access a known modified data object rather thansearching for a particular matching one.

In one embodiment, rules can be executed in two modes: (1) dynamicexecution—where there is a currently modified data object, and/or (2)static execution—where the rule analyzes the current state of an ER. Indynamic execution, the rule operates on a particular data object thathas been modified which is owned by a particular owner. In staticexecution, the rule operates on the entire actor's ER. Thus, the rulesengine 250 analyzes the data object (e.g., the data) for current stateand makes any inferences which are implied by that state.

In one embodiment, the rules engine 250 optimizes execution by (a)collapsing certain forms of logical operators that can be collapsed, and(b) reordering logical operators to achieve maximum computationalperformance. In general <and/> and <or/> are commutative with respect toone another. The rules engine 250 can also evaluate and monitor the cost(e.g., timing) of various logical operations at runtime and periodicallyreorder them to reduce the weighted average cost. For instance, valuelookups can be cached and are regarded as constants for the duration ofa rule execution event. In one embodiment, a rules engine 250 schedulermay also be used to automatically execute rules at specific frequenciesor in response to certain other criteria, such as system resources beingbelow a configurable threshold. The scheduler can automatically causethe rules engine 250 to execute rules evaluation for time-dependentcriteria. This may include age-dependent rules and others.

Thus, the rules engine 250 may use rules to categorize individuals andusers, update and notify users of the individual's health status,generate/create health maintenance actions, process action plans,generate/create information/data from other data, performinformation/data entry business logic, protective monitoring,information/data entry edit checks, select appropriate ontology conceptidentifiers, the flow of applications, support subscription-publicationservices, and present personalized content. When a rule is true, anaction may be triggered. Nonlimiting examples of rule applications andactions may include the following: generate/create content; displaycontent; modify the display content; generate/create lists;generate/create health issue data objects; generate/create healthservices data objects; generate/create a health calendar entry; updatestatus; update an action plan; generate/create an external systemmessage, trigger a secure message; trigger a reminder; invoke a contentdisplay; list an entry; send a message to an external system; send afax; supplement a list; complete a system task; add to the healthcalendar; and/or the like.

In one exemplary embodiment, the rules engine 250 manages rulesindependently of application code changes. This allows fornon-programmers to be provided with the ability to generate/create andchange rules—overcoming previous technical challenges. This ability maybe provided through an add-on decision table support. Further, multiplerule types may be supported, and an audit trail of rule changes may bemaintained.

FIG. 24 provides a flowchart for exemplary process 2400 (e.g., process2400) illustrating various processes, procedures, steps, operations,and/or the like performed by a server 65 (e.g., via execution of rulesengine 250 to automatically cause various actions to be performed). Invarious embodiments, the various actions correspond to updating and/ormanaging one or more ERs stored in the ER data store 211. Starting atstep/operation 2402, an XML rule document corresponding to a rule to beapplied to one or more ERs stored in the ER data store 211 isidentified, selected, determined, and/or the like. For instance, aplurality of rules may be defined in the ER system 100 with each rulecorresponding to an XML, rule document. To apply a rule to one or moreERs stored in the ER data store 211, the server 65 (e.g., via rulesengine 250 executing on the processing element 205) may select,identify, and/or determine an XML rule document.

At step/operation 2404, one or more ERs and/or portions thereof (e.g.,one or more SBROs of the one or more ERs) may be extracted/retrievedfrom the ER data store 211. For example, the rules engine 250 may causethe DSML 245 to retrieve, load, and/or the like one or more ERs and/orportions thereof (e.g., one or more SBROs of the one or more ERs) fromthe ER data store 211.

At step/operation 2406, the selection criteria of the rule correspondingto the selected, identified, and/or determined XML rule document isevaluated based at least in part on an ER or portion of an ER (e.g., oneor more SBROs of an ER) accessed, extracted/retrieved, and/or loadedfrom the ER data store 211 at step/operation 2404. For instance, asdescribed above, the selection criteria of a rule may comprise anexpression yielding a Boolean value. For example, the selection criteriamay include demographic criteria (e.g., subject entity gender, age, agerange, race, and/or the like). In another example, the selectioncriteria may include health circumstance criteria. Health circumstancesmay include diagnosis, having been prescribed and/or recommend a therapyand/or prescription, and/or the like. For instance, the selectioncriteria for a certain rule may be configured to only select ERs thatindicate the corresponding subject entity has particular healthcircumstances (e.g., a diagnosis, been prescribed and/or recommended atherapeutic, has had one or more particular tests performed in aparticular time period, and/or the like), is predisposed (e.g., based atleast in part on family history, test results, and/or the like) to haveparticular health circumstances, and/or the like. For example, aparticular rule may ask if a certain medication has improved a subjectentity's A1C level. The selection criteria may therefore be configuredto identify ERs corresponding to subject entities that have beenprescribed the certain medication. In another example, the selectioncriteria may be configured to identify ERs corresponding to subjectentities that have been prescribed the certain medication and had theirA1C levels tested at least one month after being prescribed the certainmedication. Thus, the selection criteria of the rule may be configuredto select ERs corresponding to subject entities for which evaluating thecondition of the rule is expected to be possible and/or for which therule is relevant to demographics and/or health circumstances indicatedby the ER corresponding to the subject entities.

At step/operation 2408, it is determined if the selection criteria aresatisfied by the ER. For instance, the rules engine 250 may determine ifthe selection criteria are satisfied by the ER based at least in part onthe evaluation of the ER based at least in part on the ER and/or portionof the ER. When it is determined, at step/operation 2408, that theselection criteria is not satisfied by the ER (e.g., <selection/> is notTrue), the process may return to step/operation 2402 or 2404 for theselection of another rule from the plurality of rules and/or foraccessing and/or loading another and/or portion thereof from the ER datastore 211.

When it is determined, at step/operation 2408, that the selectioncriteria are satisfied by the ER (e.g., <selection/> is True), theprocess continues to step/operation 2410. At step/operation 2410, thecondition of the rule is evaluated. In various embodiments, evaluationof the condition of the rule results in a Boolean value (e.g.,True/False). In various embodiments, evaluating the condition of therule may determine whether one or more test results of the ERcorresponding to the subject entity are within or outside of aparticular range (e.g., greater than a maximum threshold level and/orless than a minimum threshold level), a combination of test resultssatisfies particular criteria (e.g., test result A is greater thanthreshold A, test result B is less than threshold B, and text result Cis not less than threshold C), the ER indicates that the subject entityhas been diagnosed with a particular diagnosis, and/or variouscombinations thereof.

At step/operation 2412, it is determined if the condition criteria aresatisfied by the ER. For example, the rules engine 250 may determine ifthe condition criteria are satisfied by the ER based at least in part onthe evaluation of the ER based at least in part on the ER and/or portionof the ER. When it is determined, at step/operation 2412, that thecondition criteria is not satisfied by the ER (e.g., <condition/> is notTrue), the process may proceed to executing a <conditionFalse/> actionindicated by the rule and/or return to step/operation 2402 or 2404 forthe selection of another rule from the plurality of rules and/or foraccessing and/or loading another and/or portion thereof from the ER datastore 211. In various embodiments, executing a <conditionFalse/> actionis similar to executing a <conditionTrue/> action described below. Inparticular, executing a <conditionFalse/> action may include identifyingan OPDO (e.g., XML document) based at least in part on the<conditionFalse/> action, populating fields of the OPDO with dataassociated with the ER corresponding to the subject entity, andproviding/submitting the populated OPDO to an ingestion engine forprocessing thereby. The ingestion engine may process the populated OPDO(e.g., XML document) to cause the ER corresponding to the subject entityto be updated and/or modified based at least in part on the ER(information/data stored in association with the ER) satisfying theselection of the rule and not satisfying the condition of the rule.

When it is determined, at step/operation 2412, that the conditioncriteria are satisfied by the ER (e.g., <condition/> is True), theprocess continues to step/operation 2414. At step/operation 2414, anOPDO (e.g., XML document) is identified based at least in part on the<conditionTrue/> action. For instance, the rule may indicate an actionto be performed responsive to an ER (e.g., information/data stored inassociation with the ER) satisfying both the selection criteria and thecondition criteria of a rule. Based at least in part on the actionindicated, an OPDO is identified using an OPDO identifier. For example,the action of the rule may include an OPDO identifier (e.g., UUID or aGUID) corresponding to an OPDO. In one embodiment, the OPDO isidentified based thereon.

To execute or perform the action, one or more fields of the OPDO arepre-populated in a manner to execute or perform the action correspondingto the rule. For example, the pre-population may identify the actions tobe performed with regard to the rule in an XML structure. Thepre-population is also for reducing computational resources forperforming the action. Then, at step/operation 2416, any necessaryunpopulated fields of the OPDO are populated based at least in part oninformation/data of the ER corresponding to the subject entity for whichthe action should be executed. For instance, the rules engine 250 mayuse information/data from the evaluated ER to populate one or morefields of the OPDO. For example, the rules engine 250 mayextract/retrieve the subject entity identifier from the ER and populatea subject entity field of the OPDO (e.g., XML document) with theextracted subject entity identifier. Various other information/data maybe used to extract/retrieve information/data from the ER and used topopulate one or more fields of the OPDO. In an example embodiment, theOPDO indicates which fields of the OPDO should be populated and whichinformation/data should be extracted from the ER to populate theindicated fields of the OPDO (e.g., XML document).

At step/operation 2418, the populated OPDO (e.g., XML document) isprovided to the ingestion processing module 225. For instance, the rulesengine 250 may provide and/or submit the OPDO to the ingestionprocessing module 225. The ingestion processing module 225 may thenprocess the OPDO to generate/create a corresponding DAPDO as describedherein with regard to FIGS. 8, 12, 13, and 14 and their correspondingsteps/operations. These steps/operations are not repeated here, but areincorporated herein by reference. As will be recognized, thesteps/operations include transforming the OPDO (XML document) to a Javaobject that is executable or used for execution, generating/creating acontainer tree data structure based at least in part on the DAPDO,assembling the container tree data structure to generate/create datatransfer objects, and submitting the data transfer objects to the SBROprocessing module 265 for updating the ER corresponding to the subjectentity based thereon). Thus, the ER corresponding to the subject entityis updated based at least in part on the ER satisfying the selection ofthe rule and either satisfying or not satisfying the condition of therule. For example, the ER corresponding to the subject entity may beupdated to indicate that the subject entity should be added to aparticular group (e.g., such that the subject entity receivesinformation/data corresponding to the group, one or more flags areprovided to a provider when the provider views at least a portion of theER corresponding to the subject entity, and/or the like). For instance,if the ER corresponding to the subject entity is updated to indicatethat the subject entity should be included in a group for patients thathave recently had knee surgery (e.g., responsive to ER indicating thatthe subject entity is scheduled to have knee surgery and/or has recentlyhad knee surgery) such that the subject entity receives information/datafrom the group for patients that have recently had knee surgery (e.g.,exercises to do to help with recovery, things to watch out for, and/orthe like). After completing step/operation 2418, the process may returnto step/operation 2402 and/or 2404, in an example embodiment.

The embodiments described herein provide for efficient rules to beprogrammatically executed using a lightweight process via an XML,document to any number of ERs and/or SBROs in a computationallyefficient approach. To that end, because the <selection/> determineswhether the actor upon which the rule is currently operating is a memberof the population upon which the <condition/> will be tested and the<actions/> will be executed, it allows for the rules to be written with<conditionFalse/> actions without always executing actions whenever therules are tested because <selection/> also serves as a gate keeper tothe <condition/> evaluation. The rule context specifies the data uponwhich the rule operates, including the owning actor, and in the case ofdynamic rule running, the specific object which has been modified. Inthe case of dynamic rule running, including the modified data objectallows the rule to access a known modified data object rather thansearching for a particular matching one. And finally, by prepopulatingat least part of the action OPDO, processing time is increased forexecuting or performing the corresponding action.

Data Access/Function Controller

Various processes, procedures, steps, operations, and/or the like forupdating and managing ERs stored in an ER data store 211 have beendescribed. Various processes, procedures, steps, operations, and/or thelike for performing functions on and/or accessing information/data froman ER stored in the ER data store 211 will now be described.

In one exemplary embodiment, the ER system 100 comprises a securityarchitecture based at least in part on relationships that the ER system100 has defined and/or can infer between and/or among entities (e.g.,person to person, person to organization, and organization toorganization). The information/data a user can access as defined byrelationships and functions/operations he or she can perform on the samemay be referred to as “scope.” See FIG. 27. Scope is dynamic as more andmore information/data is known about the user. In one embodiment, scopemay be based at least in part on the requesting entity's relationship tothe subject entity. FIGS. 30A and 30B illustrate some examplerelationships between a subject entity and various other entities. Onlyrequesting entities with a “legitimate relationship” with the subjectentity are allowed access to the ER of the subject entity. In thehealthcare context, a legitimate relationship in the ER system 100 is ahealth relationship. The relationship-based approach allows securityconstraints and permissions to be defined as policies. For instance, onepolicy might be: “provider office staff can only write prescriptions ontheir patients or patients associated with their practice.” Thus, as thecare relationships are established, maintained or terminated, theappropriate access is automatically enforced. As information/data isreceived from messages and other sources, the relationships betweenentities are gleaned, codified, and used to maintain the best knowninformation/data about that relationship. In that regard, all relevantrelationships and connections can be stored and summarized into the SBROof each relationship, for instance.

FIG. 27 provides a schematic diagram illustrating how the dataaccess/function controller 255 evaluates relationships and determineswhich ERs a requesting entity may access information/data from, whichinformation/data the requesting entity may access from the ERs therequesting entity is allowed to access, and which operations/functions arequesting entity is allowed to perform with respect to theinformation/data the requesting entity may access from the ERs therequesting entity is allowed to access. FIG. 28 illustrates how arequesting entity identifier and a subject entity identifier are used tocontrol the requesting entity's access to information/data and theperformance of functions/operations for the ER corresponding to thesubject entity. For example, the requesting entity identifier and thesubject entity identifier are determined (e.g., using the identitymatching service 240) based at least in part on information/dataprovided in the ER access/function request. Based at least in part onthe determined relationship between the requesting entity and thesubject entity, a user role for the requesting entity with respect tothe subject entity is determined. Once the user role for the requestingentity is determined, a rights group(s) assigned to the user role oruser group are identified and/or determined. Each rights group includesone or more rights. Thus, once the rights group is assigned to the userrole, the rights the requesting entity (e.g., requesting user) mayexercise with respect to the ER corresponding to the subject entity maybe identified and/or determined. Each rights group or right can providean access/function key that is used to enable an access/function codemodule to cause the same to be performed with respect to the ER for thesubject entity. FIG. 37 represents examples of this.

Additionally, the security architecture comprises a number of uniquecustodial services. For example, existing systems overlook that securityshould be performed at the information/data element level in healthsystems, not the record level. Accordingly, such systems either restrictcomplete access to a patient's information/data, or restrict access to acomplete class of patient information/data (e.g., insuranceinformation/data). However, embodiments of the present invention providethe ability to restrict to any element (e.g., medical concept) ofpatient information/data. In that regard, the ER system 100 securityarchitecture is able to restrict around particular concepts (e.g., viaontology concept identifiers), the values of a field, information/dataelements, and/or combinations thereof. This permits the ER system 100 torestrict access or the display of any attribute of a data object (e.g.,SBRO) based at least in part on the attributes of an ontology conceptidentifier (or other value) defined in the graph-based ontology. As willbe recognized, the information/data may include protected healthinformation (PHI), sensitive health information, authored protectedhealth information, and/or the like. The terms PHI and/or the like areused herein to refer to information/data that relates to the past,present, and/or future physical or mental health or conditions of asubject entity; information/data that relates to the provision ofhealthcare to a subject entity; information/data that relates to thepast, present, or future payment for the provision of healthcare to asubject entity; and/or information/data that relates to that identifiesor could reasonably be used to identify the subject entity. SensitivePHI is used herein to refer to PHI that may pertain to (i) a subjectentity's HIV status or treatment of a subject entity for an HIV-relatedillness or AIDS, (ii) a subject entity's substance abuse condition orthe treatment of a subject entity for a substance abuse disorder, or(iii) a subject entity's mental health condition or treatment of asubject entity for mental illness. Authored PHI is information/dataauthored by a particular user as an event initiator or performer. Invarious exemplary embodiments, the treatment of individual healthinformation/data complies with all regulations and laws, including butnot limited to Health Insurance Portability and Accountability Act(HIPAA).

In various embodiments, the ER system 100 comprises a multi-tiered,non-hierarchical ability to restrict access or display based at least inpart on the user role or user group of a requesting entity (e.g., withrespect to the subject entity). As discussed above, in the healthcarecontext, a user role refers to the function or responsibility assumed bya person for a healthcare event (e.g., where the subject entity is thepatient within the healthcare event). In this context, user roleinformation/data documents a person's association with an identifiedhealthcare activity. User roles and/or user groups include, but are notlimited to, provider roles (e.g., admitting, attending, billing,consulting, collaborating, interpreting, performing, referring,servicing, supervising, treating), personal roles (self, next-of-kin,emergency contact, guarantor, guardian), organization roles (employee,employer, insured, subscriber), and/or the like. Further, a user canhave multiple user roles and/or be associated with multiple user groups.For instance, a user role corresponding to a particular recordaccess/function request is determined based at least in part on therelationship between the requesting entity and the subject entity of theparticular record access/function request. Each user role and/or usergroup is assigned specific rights. When the user's role or “hat”changes, the user's authorization rights dynamically change. Forexample, a doctor in his/her practice has different access rights thanthe same doctor looking at his/her own records, or the same doctoracting as a health insurance company review physician. Such an exampleis described in greater detail below with regard to Dr. Smith.Similarly, a subject entity may grant or restrict access to any or allportions of his/her ER from any and all caregivers, based at least inpart on a class of information/data (including sensitive personal healthinformation, such as, but not limited to, psychiatric information,substance abuse information, HIV status, AIDS data, and the like).Authorized users may “break the seal” on restricted information/data inemergencies if that is the appropriate disposition.

Further, the ER system 100 may permit local security administration.Each practice or distinct business entity can add and inactivate staff,and assign user roles (e.g., based at least in part on relationshipswith providers of the business entity, patients of the business entity,the organization of the business entity, and/or the like). Further, anauthorized user can delegate rights to other users, subject to thepolicies established by the custodian of the information/data.

Administrative services include the backing up of various components ofthe ER system 100, including but not limited to database files andjournal queues. Backup may be performed in stages, with frequent backupsto intermediate storage, and less frequent backups to long-term storage.Disaster recovery operations and fail-over database servers also may beused for information/data and system security and continued operations.

The ER system 100 thus allows individuals to understand and participatein their healthcare and enables caregivers and consumers to collaborateand interact using the same record in different ways. This architectureembraces the emerging roles of custodian and healthcare advocate, andassists healthcare stakeholders, including but not limited to healthsystems, health plans, IPAs, RHIOs, employers, providers, andindividuals, to meet the requirements and needs for healthcare systemsgoing forward into the future. In one exemplary embodiment, the presentinvention does not replace existing data systems and infrastructure;instead, it provides a standards-based, service-oriented infrastructurethat rapidly and easily provides health-related information/data andcomponents that work with such existing systems.

Available functions/operations to users in various exemplary embodimentsinclude, but are not limited to, identifying individuals, viewing anevent list, filtering events, detailing events, editing events, printingevents, viewing event details, managing users (e.g., adding users,editing users, editing user fields, deactivating users, identifyingusers), identifying individuals, manipulating health issues (e.g.,filtering, viewing list, viewing detail, adding, editing, and printinghealth issues), and/or the like.

To implement these capabilities, various embodiments provide a varietyof approaches to interact with the information/data in the ER system100, including, but not limited to, health portals, portlets, webservices, and/or the like. Thus, embodiments of the present inventionprovide a complete suite of information/data services and not just anend-user application. This allows embodiments of the present inventionto support existing data systems in ways that were not previouslyavailable. In one exemplary embodiment, Java standard portlets and webservices are used to provide a user interface (and user interaction)through a standard portal. A portal may be an application, a browser,mobile app, an IUI, a dashboard, a webpage, an Internetaccessible/online portal, and/or similar words used hereininterchangeably that serves as a starting point or gateway to otherresources or applications. Portlets are user interface components ormodules for a portal. Traditionally, portlets were custom applicationsfor specific portals; although recently, portlet standards (such as JSR168, which is the standard for portals running in Java) have beendefined. All interaction can take place via a communications chainextending from the portal through a portlet through the ER system 100.This architecture promotes flexibility and broad, cross-platformubiquity in terms of accommodating users.

In various embodiments, connections between the ER system 100 and ERportals and portlets may be encrypted. Such connections may be 128-bit,256-bit, and/or the like SSL-encrypted connections. In addition, thesupport connection to all custodial sites may be a VPN using encryptionand other security mechanisms to ensure that only authorized users canaccess the network, and that information/data cannot be intercepted.

FIG. 26 provides a block diagram illustrating various components relatedto controlling access to information/data and/or performingfunctions/operations on the same for one or more ERs stored in the ERdata store 211. In various embodiments a user may be a requesting entity(e.g., the requesting user) or a subject entity (e.g., the owner of theinformation/data). A user may operate a user computing entity 30 toaccess a user portal 2610. In various embodiments, the user portal 2610may comprise a plurality of user portlets 2612 (e.g., 2612A, 2612B,2612C). For instance, each user portlet 2612 may correspond to aparticular set of information/data. For instance, the user portlets 2612may include a prescription/medication portlet, a lab work/test resultsportlet, and/or various other portlets.

Additionally, the server 65 may comprise and/or operate an ER systemconnection layer 2620. Within the ER system connection layer 2620,back-end portlets 2622 (e.g., 2622A, 2622B, 2622C) may be executed. Forinstance, for each user portlet 2612 available via the user portal 2610,a corresponding back-end portlet 2622 may be operated in the ER systemconnection layer 2620. In an example embodiment, user portlet 2612Acorresponds to and/or communicates with back-end portlet 2622A, userportlet 2612B corresponds to and/or communicates with back-end portlet2622B, and/or the like. For example, a prescription/medication userportlet 2612 may interface with a prescription/medication back-endportlet 2622. The ER system connection layer 2620 interfaces with theportion of the ER system data access layer 2630. For instance, a usermay operate a user computing entity 30 to access a user portal 2610and/or a particular user portlet 2612 (e.g., via the user portal 2610).The user portlet 2612 may communicate with a corresponding back-endportlet 2622, for instance, by generating and providing an ERaccess/function request indicating (a) a request to accessinformation/data in one or more ERs, and/or (b) a request to perform afunction/operation associated with information/data in one or moreERs—both of which may be referred to herein interchangeably. In oneexample, the ER access/function request may indicate that the requestingentity has requested to access information/data from an ER correspondingto a subject entity. Additionally or alternatively, the ERaccess/function request may indicate that the requesting entity hasrequested to perform one or more functions/operations for an ERcorresponding to a subject entity.

In various embodiments, the ER access/function request may includeinformation/data identifying the user (e.g., the requesting entity),information/data identifying the subject entity corresponding to the ER(e.g., the owner of the ER), an indication of what type or set ofinformation/data is being requested, an indication of what type offunction/operation is being requested, and/or the like. The back-endportlet 2622 may pass an ER access/function request to a dataaccess/function controller 255. When the data access/function controller255 determines that the user (e.g., requesting entity) is allowed toaccess the requested information/data and/or perform the requestedfunction/operation, the ER access/function request is processed to allowthe same. In that regard, a user role for the requesting entity (e.g.,requesting user) is determined that corresponds to a rights group. Therights groups is associated with one or more rights (access toinformation/data and/or the ability to perform functions/operations).The ability to access information/data and/or perform one or morefunctions/operations is enabled via an access/function key. Theaccess/function key is used to enable an access/function code module toperform the access/function request.

When a user (e.g., requesting entity) accesses an ER system 100 accesspoint (e.g., via a user interaction event), such as a health portlet2612, the scope defined in the requesting entity's set of rights isevaluated. When the requesting entity requests information/data, onlythose permitted by the relationship are allowed by the ER system 100.For example, a requesting entity may operate a user computing entity 30to access a user portal 2610 (e.g., a user portlet 2612), which, basedat least in part on and/or responsive to the requesting entity'sinteraction with the user portal 2610 (e.g., via a user interface of theuser computing entity 30), causes the user computing entity 30 togenerate/create and provide an ER access/function request. The ERaccess/function request may be received by a corresponding back-endportlet 2622 of the ER system connection layer 2620. The ERaccess/function request may be processed to extract/retrieveinformation/data identifying the requesting entity from the ERaccess/function request and to extract/retrieve information/dataidentifying the subject entity from the ER access/function request. Theinformation/data identifying the requesting entity and theinformation/data identifying the subject entity (e.g., that wasextracted from the ER access/function request) may then be passed toinstances of the identity matching service 240 to determine and/oridentify a requesting entity identifier corresponding to the requestingentity and a subject entity identifier corresponding to the subjectentity. In such a case, the process 1200 may be invoked to identify theentity identifiers for any relevant entities. These steps/operations arenot repeated here, but are incorporated by reference. Alternatively, atleast one of the entity identifiers may be stored and provided from auser profile (triggered by a user interaction event). Based at least inpart on the requesting entity identifier and the subject entityidentifier, a relationship between the requesting entity and the subjectentity is determined, examples of which are provided by at least FIGS.30A and 30B. Based at least in part on the determined relationshipbetween the requesting entity and the subject entity, a user role forthe requesting entity with respect to the subject entity is determined.Each user role of the ER system 100 is assigned to one or more rightsgroups. Each rights group comprises one or more rights. A rights groupor a right can be defined in a rights group data object, such as an XML,document that comprises a corresponding access/function key for therights group or the right. Each access/function associated with an ERmay require the use of an access/function key to access the calling ofthe access/function code module. Thus, the requesting entity is onlyable to cause functions/operations to be performed that correspond tothe rights in a rights group assigned to a user role that describes therole of the requesting entity in a relationship with the subject entity.

In one embodiment, FIG. 29 provides a flowchart for exemplary process2900 (e.g., data access/function control process 2900) illustratingprocesses, procedures, steps, operations, and/or the like performed fora user (e.g., a requesting entity) to allow access to a set ofinformation/data and/or the performance of one or more functions on anER (e.g., of a subject entity) stored in the ER data store 211.

Starting at step/operation 2902, a user operating a user computingentity 30 (e.g., operated by a user) may access a user portlet 2612, andthe ER system 100 initializes the selected application and validates thecredentials of the user. For instance, after logging on, the ERaccess/function request may be to access medication information/data ina user's ER and/or prescribe medication to the user (e.g., perform afunction/operation). In one embodiment, the user portlet 2612 may promptthe user (e.g., requesting entity) for identification of thepatient/subject entity if the patient/subject entity has not alreadybeen identified. Upon selection of the patient/subject entity, the userportlet 2612 sends an ER access/function request, for example. Inresponse, the user portlet 2612 generates a web service request, forexample, on behalf of the user (e.g., the requesting entity).

At step/operation 2904, the server 65 receives the ER access/functionrequest generated from the user portlet 2612. The server 65 determines arequesting entity identifier corresponding to the requesting entity(e.g., the user) whose interaction with the user portlet 2612 caused theER access/function request to be generated/created and provided. Theserver 65 also determines a subject entity identifier corresponding tothe subject entity corresponding to the ER that is to be acted upon viathe access/function request. For example, information/data identifyingthe requesting entity and/or the subject entity may be extracted fromthe ER access/function request. The information/data identifying therequesting entity may be passed to the identity matching service 240(e.g., via an invoking API call (e.g., an API request)), and acorresponding requesting entity identifier may be received from theidentity matching service 240 (e.g., via an API response). Similarly,the information/data identifying the subject entity may be passed to theidentity matching service 240 (e.g., via an invoking API call (e.g., anAPI request)), and a corresponding subject entity identifier may bereceived from the identity matching service 240 (e.g., via an APIresponse). In another embodiment, the entity identifier may be stored aspart of a user profile, such that the entity identifier(s) isautomatically provided when logging into or accessing the ER system 100.

At step/operation 2906, the data access/function controller 255determines a relationship between the requesting entity and the subjectentity. In a particular embodiment, this may include determining arelationship “type” and a relationship “status.” As will be recognized,determining relationship types and statuses between or among entitiescan be performed using a variety of techniques and approaches.

In one embodiment, relationships (including relationship types andstatuses) may be determined using a graph data structure. For example,the ER system 100 may store a graph data structure (e.g., relationshipgraph) representing information/data representing relationships betweenvarious entities (stored as nodes and edges, for instance). FIG. 30Arepresents a portion of such a graph data structure (e.g., relationshipgraph). In an example embodiment, the ER data store 211 storesrelationship SBROs that provide information/data corresponding torelationships between pairs of entities. The graph data structure (e.g.,relationship graph) may encode at least a portion of theinformation/data stored in the relationship SBROs. For example, thenodes of the graph data structure (e.g., relationship graph) may beentities and the edges connecting the nodes may encode information/datastored in the corresponding relationship SBROs. If a relationship SBROdoes not exist for a pair of entities, the graph data structure (e.g.,relationship graph) does not include an edge directly (e.g., without anyintermediate nodes) connecting the pair of entities.

In the graph context, the data access/function controller 255 (e.g.,executing on the server 65) uses the requesting entity identifier, thesubject entity identifier, and the graph data structure (e.g., therelationship graph) to determine a relationship “type” between therequesting entity and the subject entity. For instance, the requestingentity may be identified in the graph data structure (e.g., relationshipgraph) based at least in part on the requesting entity identifier andthe subject entity identifier in the graph data structure (e.g.,relationship graph). The relationship “type” between the requestingentity and the subject entity is then determined by evaluating thepreferred path (e.g., the nodes and edges) between the requesting entityand the subject entity through the graph data structure (e.g.,relationship graph). In various embodiments, the preferred path betweenthe requesting entity and the subject entity through the graph datastructure (e.g., relationship graph) is the path that includes the leastnumber of nodes (e.g., intermediate nodes) and edges. For example, a“direct” relationship (e.g., having no intermediate nodes between therequesting entity and the subject entity) is preferred over a path thatincludes one or more intermediate nodes (an “indirect” relationship). Inan example embodiment, the “preferred path” through the graph datastructure (e.g., relationship graph) is the path between the requestingentity and the subject entity through the graph data structure (e.g.,relationship graph) that (a) corresponds to the most recently updatedrelationship SBROs and/or (b) has the least number of intermediatenodes. For instance, a path where the SBROs corresponding to all of theedges have been updated within the past year may be preferred to a pathwhere one or more of the SBROs corresponding to edges of the path havenot been updated in more than five years. In an example embodiment, thepreferred path between the requesting entity and the subject entitythrough the graph data structure (e.g., relationship graph) isdetermined based at least in part on minimizing the number ofintermediate nodes.

In the graph context, the data access/function controller 255 (e.g.,executing on the server 65) uses the requesting entity identifier, thesubject entity identifier, and the graph data structure (e.g.,relationship graph) to determine a relationship “status” between therequesting entity and the subject entity. For example, minimizing thenumber of edges having a status of inactive may also be a preferred pathbetween the requesting entity and the subject entity. In an exampleembodiment, only certain types of relationships having a status of“inactive” are included in the graph data structure (e.g., relationshipgraph). For example, if a particular physician used to be the attendingphysician for a particular patient, the relationship between theparticular physician and the particular patient may be included in thegraph data structure (e.g., relationship graph). However, if a nurseused to work for the particular physician but no longer does, therelationship of the nurse being a staff member of the particularphysician's practice is not be included in the graph data structure(e.g., relationship graph). In that regard, the data access/functioncontroller 255 may determine whether the relationship “status” betweenthe requesting entity and the subject entity is “active” or “inactive”based at least in part on the determined preferred path and/or otherindicators. For example, one or more edges along the preferred path maybe examined to determine if the relationship between the requestingentity and the subject entity is active or inactive. For instance, ifthe requesting entity is a physician that, according to thecorresponding SBRO, was a former attending physician for the subjectentity, the relationship between the requesting entity and the subjectentity is determined to be “inactive.” Similarly, if the requestingentity is a staff member in the practice of a physician that, accordingto the corresponding SBRO, was a former attending physician for thesubject entity, the relationship between the requesting entity and thesubject entity is determined to be “inactive.” However, if therequesting entity is a staff member in the practice of a physician that,according to the corresponding SBRO, is a current attending physicianfor the subject entity, the relationship between the requesting entityand the subject entity is determined to be “active.” Alternatively, eachrelationship SBRO may comprise a start date (e.g., 2020-02-0100:00:00.000) and an end date for the relationship (e.g., or 2020-07-0100:00:00.000 or 2021-01-01 00:00:00.000). In such a case, if the currenttime and date are within the start and end date of each relationshipdata object, the relationship status is “active.” However, if the startdate is in the future or in end date is in the past, the relationshipstatus is “inactive.”

In another embodiment, relationships (including relationship types andstatuses) may be determined using one or more relationship data objects.For example, the ER system 100 may comprise or have access to arelationship table (e.g., accessible via the DSML 245) representinginformation/data representing relationships between various entities(stored as relationship data objects, for instance). In an exampleembodiment, the ER data store 211 stores relationship SBROs that provideinformation/data corresponding to relationships between pairs ofentities. The relationship data objects may encode at least a portion ofthe information/data stored in the relationship SBROs. For example, agiven entity may have a respective relationship data object for everyrelationship. Returning to FIG. 30, if these relationships wererepresented as data objects in a table for Patient A, there would be:(1) a relationship data object representing the relationship betweenPatient A and Provider B; (2) a relationship data object representingthe relationship between Patient A and Practice C; (3) a relationshipdata object representing the relationship between Patient A and ProviderF; (4) a relationship data object representing the relationship betweenPatient A and Nurse D; and (5) a relationship data object representingthe relationship between Patient A and Clinic E. Similarly, for ProviderB, there would be a relationship data object representing therelationship between Provider B and Patient A. For Practice C, therewould be a relationship data object representing the relationshipbetween Practice C and Patient A. For Provider F, there would be arelationship data object representing the relationship between ProviderF and Patient A. For Nurse D, there would be a relationship data objectrepresenting the relationship between Nurse D and Patient A. And forClinic E, there would be a relationship data object representing therelationship between Clinic E and Patient A. This structure allows forparallel or serial queries for both entities to determine theirrelationship. For instance, the query for Patient A can be used toretrieve the relationship data objects for Patient A and so on.

In the relationship data object context, the data access/functioncontroller 255 (e.g., executing on the server 65) uses the requestingentity identifier and the subject entity identifier to determine arelationship “type” between the requesting entity and the subjectentity. For instance, the requesting entity identifier and the subjectentity identifier may be used to generate two separate queries: onequery for the requesting entity to determine the relationship with thesubject entity and another query for the subject entity to determine therelationship with the requesting entity. In one embodiment, to increaseoperational efficiency, the data access/function controller 255 may usea database index for each query. Indexes allow for the efficientlocation of information/data without having to search every row in adatabase table, for instance, every time a database table is accessed.Such indexes may include clustered indexes, non-clustered indexes,and/or the like. In a clustered index, the index may be implemented as abinary tree data structure where ERs are stored in order according tothe clustered index that has one index per table. In a non-clusteredindex, the index may also be implemented as a tree data structure whereERs are stored in an order that is different than the non-clusteredindex, where there may be multiple indexes per table. Continuing withthe above example, if a relationship exists between the requestingentity and the subject entity, two relationship data objects will bereturned that represent the relationship. The relationship data objectfor the requesting entity defines the relationship with the subjectentity, and the relationship data object for the subject entity definesthe relationship with the requesting entity. Returning to FIG. 30A,Patient A's ER will have a relationship data object for Provider B(indicating a direct relationship), and Provider B's ER will have arelationship data object for Patient A (indicating a directrelationship). Each relationship data objects defines the correspondingrelationship, and the relationship data objects should comprise the samerelationship type, which can be used as an additional security measure.For example, once each relationship data object has been returned, thedata access/function controller 255 determines whether the relationshipdata objects both indicate the same “type” of relationship.

In the relationship data object context, the data access/functioncontroller 255 (e.g., executing on the server 65) uses the returnedrelationship data objects determine a relationship “status” between therequesting entity and the subject entity. For example, each relationshipdata object may comprise a start date (e.g., 2020-02-01 00:00:00.000)and an end date for the relationship (e.g., or 2020-07-01 00:00:00.000or 2021-01-01 00:00:00.000). In such a case, if the current time anddate are within the start and end date of each relationship data object,the relationship status is “active.” However, if the start date is inthe future or in end date is in the past, the relationship status is“inactive.” Thus, once each relationship data object has been returned,the data access/function controller 255 determines whether therelationship data objects both indicate the same “status” ofrelationship. As will be recognized, each relationship data object maybe an SBRO for each respective entity.

At step/operation 2908, the user role of the requesting entity for therelationship between the requesting entity and the subject entity isdetermined. For example, based at least in part on the relationship typeand relationship status, the data access/function controller 255 (e.g.,executing on the server 65) determines a user role for the requestingentity with respect to the relationship between the requesting entityand the subject entity. As described above, a variety of user roles maybe defined within the ER system 100. The user roles may be businessentity specific (e.g., generated/created by a business entity and onlyapplied in relationships including the business entity), globallydefined (e.g., may be applied to various relationships defined with theER system 100), and/or the like.

Continuing with the above, one user may have one or more user roles andcorresponding rights groups with respective rights. For example, Dr.Smith may be a managed care medical director, an on-call trauma surgeon,and a general surgeon. As a managed care medical director, Dr. Smith'sfirst user role (e.g., scope) may be as an employee of a large insurancecompany and may access those ERs where the subject entity's insurancename matches the employer name of the user. In the managed care medicaldirector context, Dr. Smith may be able to perform a first set offunctions/operations (e.g., claims, eligibility, and referrals) andaccess a first set of data types (e.g., all data and protected data). Asan on-call trauma surgeon, Dr. Smith's second user role (e.g., scope)may be as an employee of a medical center and may access those ERs wherethe subject entity's attending physician matches the attending physicianof user. In the on-call trauma surgeon context, Dr. Smith may be able toperform a second set of functions/operations (e.g., allergies, events,medications, and problems) and access a second set of data types (e.g.,all data and protected data with override). And finally, as a generalsurgeon, Dr. Smith may have a third user role (e.g., scope) as a generalsurgeon practice and may access those ERs where the subject entity'sreferral physician matches the physician name of the user. In thegeneral surgeon practice context, Dr. Smith may be able to perform athird set of functions/operations (e.g., allergies, claims, events,medications, problems, and referrals) and access a third set of datatypes (e.g., all data and data user authored and protected data granteduser by patient).

At step/operation 2910, one or more rights groups assigned to thedetermined user role are determined. For instance, the dataaccess/function controller 255 may determine one or more rights groupsassigned to the determined user role. For example, each user role may beassociated with and/or have assigned thereto one or more rights groups.For instance, a user role definition may include one or more rightsgroup identifiers that identify rights group data objects, such as XML,documents for the rights groups (an corresponding rights) assigned tothe user role. As noted, each rights group comprises or defines one ormore rights, such as described above with regard to Dr. Smith. See FIG.37.

In various embodiments, each rights group data object may also identifyor be associated with one or more access/function keys. Theaccess/function keys can be used to enable corresponding access/functioncode modules to perform the ER access/function request (e.g., read,write, modify, delete, and/or the like). For example, as described, arights group data object may be an XML document or other data objectcomprising a description of the rights group or right, theaccess/function key, the access/function code module, and/or the like.In one embodiment, the access/function key is used to perform the ERaccess/function request. In another embodiment, the access/function keyis used to enable the corresponding access/function code module toperform the ER access/function request. In this embodiment, atstep/operation 2912, the access/function key is used to enable thecorresponding access/function code module to perform the ERaccess/function request. Consequently, the access/function code modulemay then be used to perform the ER access/function request. As will berecognized, the ER access/function request may vary depending on thecontext and needs of the user (e.g., requesting entity).Steps/operations 2914, 2916, 2918, 2920, and 2922 provide some examples.

In one embodiment, to display, provide, and/or extract/retrieveinformation/data (e.g., show medication information/data), the ERaccess/function request is transformed into an EPDO, at step/operation2914. In various embodiments, an EPDO is similar to an OPDO, exceptthat, rather than comprising observations (e.g., ontology conceptidentifiers and corresponding values), the EPDO comprises extractables(e.g., ontology concept identifiers for which values should be accessedform the ER corresponding to the subject entity). And similar to OPDOs,EPDOs are used to generate/create DAPDOs by the extraction processingmodule 260. For example, at step/operation 2916, the EPDO can besubmitted to the extraction processing module 260. The extractionprocessing module 260 (e.g., executing on the server 65) processes theEPDO to create/generate a DAPDO, which is in turn used togenerate/create one or more observation groups, such as is describedwith regard to process 3100. For example, the extraction processingmodule 260 may process the DAPDO to determine which information/data toaccess and invoke and/or cause the DSML 245 to interface with thepersistent storage of the ER data store 211 to retrieve the requestedinformation/data. For instance, the generated/created observation groupsmay include observations (e.g., ontology concept identifiers andcorresponding values) that correspond to the extractables of the EPDOand have values extracted from the ER corresponding to the subjectentity.

At step/operation 2918, the generated/created one or more observationgroups are provided to the back-end portlet 2622. The back-end portletmay generate/create a response based at least in part on the one or moreobservation groups. For example, the response may include theinformation/data of the one or more observation groups. In an exampleembodiment, the response is generated/created in a source vocabularycorresponding to the user computing entity 30, the user portal 2610, therequesting entity, and/or the like. For instance, the back-end portlet2622 may transform the generated/created observation groups into asource vocabulary corresponding to the user portal 2610. The back-endportlet 2622 may then provide the response such that the user portlet2612 receives the response. The user portlet 2612 may receive theresponse. Responsive to the user portlet 2612 receiving and/orprocessing the response, the user computing entity 30 (e.g., via display316 and/or another user output device/interface) may provide at least aportion of the response (e.g., the information/data contained thereinand accessed from the ER corresponding to the subject entity).

In another embodiment, at step/operation 2920, the access/function codemodule may then be used to perform the ER access/function request (e.g.,prescribe a medication). That is, the appropriate access/function codemodule allows the requesting entity to prescribe a medication for thesubject entity. And as a result, at step/operation 2922, the completionof the same can be presented to the user (e.g., requesting entity) via auser portlet 2612, for example. As will be recognized, a variety ofother approaches an techniques can be used to adapt to various needs andcircumstances.

Various embodiments of this security architecture provide technicalsolutions to technical problems related to controlling access to andfunctions/operations on information/data stored in the ER data store211. In particular, the graph data structure (e.g., relationship graph)enables efficient and effective determinations of the relationshipbetween a requesting entity and a subject entity and various attributesof the relationship (e.g., active/inactive, direct/indirect, and/or thelike). Based at least in part on the relationship and relationshipattributes represented by the graph data structure (e.g., relationshipgraph), a user role for the requesting entity with respect to thesubject entity may be determined. Similarly, the relationship dataobjects provide for increased operational determinations and efficiency.Accordingly, ER access/function requests for a user are limited by therights group assigned to the user role determined for the requestingentity with respect to the subject entity. Thus, for different subjectentities, the functions that a requesting entity (e.g., requesting user)may cause to occur (e.g., to the ER corresponding to the subject entity)may be different. Moreover, as a relationship between the requestingentity and the subject entity changes with time, the permissible ERaccess/function requests the requesting entity (e.g., requesting user)may carry out (e.g., to the ER corresponding to the subject entity) mayalso change. By using graph data structure (e.g., relationship graph),this architecture provides a flexible structure may be updated ascorresponding relationship SBROs are updated. Further, this allows usersroles for any entity respect to the subject entity to be efficientlymaintained and remain up-to-date in an automated manner. Variousembodiments therefore provide improvements to the automated managementof user access to information/data stored in the ER data store 211.

Extraction Processing

In various embodiments, the extraction processing module 260 is executedby the server 65 to enable access to information/data stored in an ER toa user operating a user computing entity 30. For instance, theextraction processing module 260 is configured to process an EPDO (seeEPDO 1002 of FIG. 10C) and thereby cause the requested function and/oroperation corresponding to the ER access/function request to beperformed. For example, the extraction processing module 260 may processan EPDO to generate a DAPDO to cause information/data to be accessed,read, and/or extracted from an ER to generate/create one or moreobservation groups.

FIG. 31 provides a flowchart for exemplary process 3100 (e.g., dataextraction process 3100) illustrating various processes, procedures,steps, operations, and/or the like performed by an extraction processingmodule 260 operating on and/or being executed by a server 65 to processa DAPDO to generate/create populated observation groups that may be usedto generate/create a response to record access/function request.Starting at step/operation 3102, the extraction processing module 260receives an EPDO. For instance, an access/function code module accessedusing an access/function key via the data access/function controller 255may generate/create an EPDO based at least in part on an ERaccess/function request received via the ER system connection layer2620. The access/function code module and/or the data access/functioncontroller 255 may then provide the EPDO, such that the extractionprocessing module 260 receives the EPDO. The EPDO typically comprises asubject entity identifier and one or more ontology concept identifiers(e.g., extractable identifiers) corresponding to elements ofinformation/data to be accessed from the ER corresponding to the subjectentity.

At steps/operations 3104 and 3106, information/data is read from theEPDO and used to generate/create a DAPDO, as has been previouslydescribed with regard to FIGS. 8, 12, 13, and 14 and their correspondingsteps/operations. For example, the EPDO (along with otherinformation/data) is transformed from an XML document used for storageand transmission to a Java object that is executable or used forexecution, such as via unmarshalling. Thus, the DAPDO comprises thestructure of the EPDO with additional information/data that allows forthe construction of a container tree data structure to extract/retrieveinformation/data identified in the EPDO. For example, each DAPDOcomprises a DAPDO identifier, information/data from the correspondingEPDO, a DAPDO type (indicate the information/data it contains), anowner, and/or the like. This allows the DAPDO to be generated/createdwith the necessary information/data. The steps/operations of FIGS. 8,12, 13, and 14 are not repeated here, but are incorporated by reference.

The extraction processing module 260 may also generate/create dataobjects comprising one or more empty observation groups (e.g.,observation groups where values are not associated with the graph-basedontology concept identifiers). In various embodiments, the emptyobservation groups are generated/created based at least in part onextractables indicated in the DAPDO. For instance, based at least inpart on the extractables indicated in the DAPDO, empty observationgroups may be generated/created in a manner similar to generatingobservation groups observables in DAPDO (for ingestion), but with thevalues of the corresponding observables being empty. In an exampleembodiment, a data object is generated/created for each emptyobservation group corresponding to the DAPDO. In an example embodiment,a data object may comprise a plurality (e.g., two or more) of emptyobservation groups corresponding to the DAPDO.

At step/operation 3108, the extraction process 3100 constructs an emptycontainer tree data structure corresponding to the empty observationgroup(s). In various embodiments, a data artifact packet container nodeis generated/created. The data artifact packet container node is thereceptacle that holds all other container nodes of the empty containertree data structure and is the root node of the empty container treedata structure. The graph-based domain ontology defines ontologyconcepts with appropriate selectable extractables to generate/create theempty container tree data structure. For example, container nodes of theempty container tree data structure comprise extractables correspondingto the observation group(s) of each data object. Each of the containernodes in the empty container tree data structure contains extractablesused to populate the empty container tree data structure. Containernodes are recursive but dependent upon the graph data structurerepresenting the graph-based ontology. For instance, the empty containertree data structure comprises a plurality of nested container nodes heldwithin the root node (e.g., the data artifact packet tree datastructure). The leaves of the empty container tree data structure (whichat this stage are empty) are values corresponding to the graph-basedontology concepts (e.g., identifiable by the concept identifiers) of thecontainer node comprise the leaf node. For example, the extractables inan empty observable group are related to one another in a mannerdescribed in the graph data structure representing the graph-basedontology. This graph data structure defines which container nodes can besubcontainer nodes of other container nodes. For instance, an entity inthe corresponding data object corresponds to an entity container node inthe empty container tree data structure and the entity's name(s)corresponds to a subcontainer node of the entity container node (e.g.,an entity name container). However, the reverse would never be true. Inparticular, an entity name container would never be a subcontainer nodeof an entity container node because this structure is not supported bythe graph-based ontology or the graph data structure representing thegraph-based ontology. In an example embodiment, the empty container treedata structure is constructed in a manner similar to that shown in anddescribed with respect to FIG. 14—with at least two differences. For thefirst difference, process 1308 eliminates any observable (containernode) from the tree construction that has a null value and does not havea property defined (used for defaults). In contrast, the treeconstruction of process 3100 only has empty observation values and willconstruct nodes for all submitted observations into a container treedata structure. For the second difference, process 1308 will not querygraph-based ontology for any property definition in an attempt topopulate default values; however, process 3100 will. Thus, process 1308is incorporated herein by reference with these two exceptions. Thus, theempty container tree data structure is constructed as described withregard to process 1308 with these two exceptions.

Once the empty container tree data structure has been constructed basedat least in part on the observation group(s), the container nodes of thecontainer tree data structure are disassembled (e.g., via a disassemblyprocess) at step/operation 3110 to populate the empty container treedata structure (e.g., to generate/create a populated container tree datastructure). For instance, the extraction processing module 260 adds thepolymorphic disassemble method to the container nodes to execute thedisassembly process. Thus, the disassembly process is responsible foracquiring the value for the extractable represented by that containernode. As discussed, a container node either represents a specificelement of ER information/data or it has subcontainer nodes thatrepresent parts of the value represented by the container node. In oneembodiment, the disassembly process may cause values corresponding tothe extractables of the corresponding data object (e.g., as representedby the empty container tree data structure) to be extracted, read,and/or accessed from the ER (e.g., via the DSML 245) corresponding tothe subject entity and cause the empty container tree data structure tobe populated with the values such that a populated container tree datastructure is constructed.

In various embodiments, population is the process by which theextraction processing module 260 acquires values (e.g., via the DSML245) for the extractables specified in the observation group(s) of thecorresponding data object and populates in the values in each containernode of the empty container tree data structure to generate/create apopulated container tree data structure. This process is referred to asdisassembly herein, and in an example embodiment, is the inverse of theingestion processing module 225 assembly process described above. Forexample, an empty container tree data structure may be populated togenerate/create a populated container tree data structure by invoking adisassembly get function on the data artifact packet tree datastructure. This may initiate a cascade within each subcontainer node,causing each subcontainer node to be evaluated down to the leaves of theempty container tree data structure. In various embodiments, acquiringvalues for container nodes comprises retrieving a value directly fromthe subject entity's ER or in aggregating the values from subcontainernodes. There may also be additional observations to be populated in aparticular container in addition to the persisted ER information/data.The end effect of invoking the disassembly function on the data artifactpacket tree data structure is a fully populated container tree datastructure from which one or more populated observation groups aredetermined and/or generated/created. When an extractable or group ofextractables represents a collection, the extraction processing module260 returns all elements of the collection in an observation group, witheach element of the collection in its own child observation group.

In various embodiments, an ontology concept has three attributes whichare persisted: the source vocabulary, source vocabulary code, and anoptional text description. In various embodiments, disaggregators areused to produce the source vocabulary, source vocabulary code, andoptional text description corresponding to an ontology conceptidentifier of the empty container tree data structure. When thepopulated container tree data structure is converted into one or morepopulated observation groups, the source vocabulary, source vocabularycode, and optional text description may be provided as child observationgroups of a populated observation group.

In various embodiments, to prevent recursions and attempts todisassemble container nodes with errors, during disassembly, a containerexists in one of four states: not disassembled (disassembly of thecontainer has not yet been attempted), undergoing disassembly (thecontainer is currently being disassembled), disassembled (the containerhas been successfully disassembled), and unable to disassemble (theattempt to disassemble the container has resulted in an exception). Forinstance, if, during disassembly, it is determined that the value of acontainer cannot be extracted from the ER corresponding to the subjectentity (e.g., based at least in part on the requesting entity not beingauthorized to access the value, the value not being present in the ERcorresponding to the subject entity, and/or the like), the containernode may be determined to have an exception and be assigned the statusof unable to disassemble. Any exceptions or warnings encountered duringthis process are added to the appropriate collections (e.g., a warningscollection, an exceptions collection, and/or the like). For example,when a container is assigned the state unable to disassemble, anexception or warning may be logged such that the manual review may beperformed. In various embodiments, exceptions are used to associate aJava exception with an ontology concept. This allows furtherinformation/data such as expanded descriptions and suggestedresolutions.

In various embodiments, the populated container tree data structure isconverted into and/or used to determine and/or generate/create one ormore populated observation groups. For instance, the values extractedfrom the ER corresponding to the subject entity and/or aggregated fromsubcontainer nodes may be used to populate the empty observation groupsand/or to generate/create populated observation groups.

In various embodiments, the extraction processing module 260 is executed(e.g., by the processing element 205 of the server 65) togenerate/create one or more populated observation groups based at leastin part on the population of the empty container tree data structure bytraversing the empty container tree data structure in a depth-firsttraversal. For example, the empty container tree data structure may betraversed starting at a leaf node (e.g., to populate the value of acontainer based at least in part on extracting a value from the ERcorresponding to the subject entity) upward to the root of the emptycontainer tree data structure. For instance, the values of a firstcontainer requiring extraction from the ER corresponding to subjectentity may be used and/or aggregated to determine and/or generate/createa value for a container for which the first container is a subcontainer.By way of example, systolic blood-pressure and diastolic blood-pressurecan be separate retrieved from corresponding leaf nodes and aggregatedup to a subcontainer node with a single blood-pressure reading. Thus,each value is respectively retrieved and aggregated up the containertree data structure to form a single blood-pressure reading. In anexample embodiment, the result of invoking the disassembly process onthe empty container tree data structure is the generation of one or morepopulated observation groups.

After completing the disassembly process (e.g., once all the containernodes have a status of one of disassembled and/or unable to disassemble)and/or responsive to completing the disassembly process, the extractionprocessing module 260 provides the one or more populated observationgroups generated/created for use in generating a response to the ERaccess/function request, at step/operation 3112. For example, theextraction processing module 260 may provide the one or more populatedobservation groups for use in generating a response that is provided toa user computing entity 30, via messages, transfer data objects,interfaces, and/or the like. In an example embodiment, the back-endportlet 2622 via which the ER access/function request was receivedreceives the populated observation groups and generates and provides theresponse. For instance, the back-end portlet 2622 may generate/createthe response (possibly in a source vocabulary corresponding to therequesting entity, the user computing entity 30, a user portal 2610,and/or the like) and provide the response such that the user computingentity 30 receives the response and provides (e.g., displays and/oraudibly provides) at least a portion of the response via a userinterface thereof.

As will be recognized, after extraction, each DAPDO used for extractioncan be stored in a data store archive that allows for a complete audithistory of all access and functions associated with informationinformation/data in the ER system 211. In other words, by persisting acopy of each DAPDO, the source of any activity can be traced to a DAPDOand requesting entity. As will be recognized, a variety of otherapproaches and techniques can be used to adapt to various needs andcircumstances.

Exemplary Interfaces and Outputs

FIGS. 32-36 provide some example views of various user interfaces thatmay be provided via user portal 2610 and/or portlets 2612.

FIG. 32 provides an example view of a provider (e.g., physician)dashboard and/or landing page 3200 provided via a user portal 2610. Forexample, when a provider (e.g., physician) operates a user computingentity 30 to log into and/or be authenticated by the user portal 2610(e.g., a provider/physician portal), the portal 2610 may cause a userinterface of the user computing entity 30 (e.g., display 316) to displaythe dashboard or landing page 3200. The dashboard and/or landing page3200 comprises a patient search portlet 3202 that may be used to searchfor information/data corresponding to an individual and/or to accessinformation/data from an electronic record corresponding to a patient.The dashboard and/or landing page 3200 comprises an appointment list3204 indicating the upcoming appointments the provider (e.g., physician)scheduled for the provider. The information/data provided in theappointment list 3204 may be pulled and/or accessed from a practicemanagement system corresponding to the provider (e.g., physician) ratherthan from the ER system 100, in an example embodiment.

The dashboard and/or landing page 3200 comprises a task list 3206. Thetask list may indicate tasks to be performed by the provider (e.g.,physician) other than attending the scheduled appointments. For example,the task list 3206 may indicate tasks such as refilling prescriptionsand/or authorizing repopulates of prescriptions, reviewing lab work orother test results, and/or the like. In various embodiments, the itemslisted in the task list 3206 are determined based at least in part onchanges made to ER corresponding to patients of the provider (e.g.,physician). For example, if lab work that was ordered by the provider(e.g., physician) for a first patient was recorded in the ERcorresponding to the patient (e.g., the patient is the subject entityassociated with the ER), a review lab work task may be populated on thetask list 3206 for the provider (e.g., physician) via the task portletthat provides the task list 3206. For example, when the provider (e.g.,physician) logs into and/or is authenticated by the portal 2610, the ERsystem 100 may determine (e.g., via one or more change logs, modifiedcollections, and/or the like) what updates and/or modifications havebeen made to ER associated with subject entities for which the provider(e.g., physician) has an active relationship associated with the userrole of attending physician, where the updates and/or modifications havebeen made since the provider last logged in to and/or was authenticatedby the portal. The ER system 100 may then analyze the identified updatesand/or modifications using a rules engine to determine what, if any,actions should be taken based at least in part on the identified updatesand/or modifications. The task list 3206 may provide a result of thatanalysis of the updated and/or modifications that correspond to anaction being taken on the part of the provider (e.g., physician). In anexample embodiment, the action to be taken on the part of the provider(e.g., physician) is simply viewing at least a portion of an ER that hasbeen updated and/or modified so that the provider (e.g., physician) ismade aware of the update and/or modification. In an example embodiment,a user (e.g., the provider/physician) may mouse over or select (e.g.,click) an item on the task list 3206 or an item on the appointment list3204 to cause an item detail view 3208 to be provided. In variousembodiments, the item detail view 3208 is a pop-up window, an embeddedwindow, a dialog box, and/or the like.

FIG. 33 provides an example individual ER view 3300 provided via a userportal 2610. For example, via the dashboard and/or landing page 3200,the provider (e.g., physician) may use the patient search portlet 3202to search for a particular patient and view the ER for which theparticular patient is the subject entity. The provider (e.g., physician)may then be provided with the individual ER view 3300 of the user portal2610. In an example embodiment, the individual ER view 3300 comprises apatient information section 3302 that provides demographicinformation/data corresponding to the patient. For example, the patientinformation section 3302 may include the patient name; birthdate; age;sex, gender, and/or preferred pronouns; and/or the like. The individualER view 3300 may comprise an upcoming appointment section 3304indicating any upcoming appointments schedule for the patient to meetwith the provider (e.g., physician) and/or a staff member (e.g., nurse,lab technician, and/or the like) of the provider's practice. Theindividual ER view 3300 may comprise a care team section 3306 thatindicates other providers, care givers, case workers, and/or the likethat are currently providing care to the patient. In an example,embodiment, the information/data provided in the care team section 3306is determined based at least in part on relationships between thepatient and other providers indicated by relationship SBROs stored inthe data storage 211.

The individual ER view 3300 may further comprise a plurality of portlets(e.g., user portlets 2612). For example, the individual ER view 3300 maycomprise a “recent conditions” portlet view 3308 which may provide alist of current and/or recent diagnoses for the patient. For example,the individual ER view 3300 may comprise a “health indicators” portletview 3310 which may provide a list of test and/or lab work results,periodic tests and/or exams, and/or the like. For example, theindividual ER view 3300 may comprise an “allergies” portlet view 3312which may provide a list of any known allergies of the patient. Forexample, the individual ER view 3300 may comprise a “currentmedications” portlet view 3314 that may provide a list of medicationscurrently being taken by and/or prescribed to the patient. For example,the individual ER view 3300 may comprise a “recenttests/exams/treatments” portlet view 3316 that lists the results ofrecent texts/exams/treatments for the patient. For example, theindividual ER view 3300 may comprise a “visit history” portlet view 3318which may provide a list summarizing the patient's recent visits withproviders of the patient's healthcare team (e.g., as indicated in thecare team section 3306). In various embodiments, the information/dataprovided in the “recent conditions” portlet view 3308, “healthindicators” portlet view 3310, “allergies” portlet view 3312, “currentmedications” portlet view 3314, “recent tests/exams/treatments” portletview 3316, and/or “visit history” portlet view 3318 may be accessed fromthe ER associated with the subject entity identifier corresponding tothe patient and stored in the data storage 211. For example, theinformation/data provided in the “recent conditions” portlet view 3308,“health indicators” portlet view 3310, “allergies” portlet view 3312,“current medications” portlet view 3314, “recent tests/exams/treatments”portlet view 3316, and/or “visit history” portlet view 3318 may bepopulated through a process similar to that described with respect toFIG. 32.

In various embodiments, the user portal 2610 may further comprise avariety of other views that may provide a provider with specific testresults, additional information/data about a selected medication,reports corresponding to care and/or test/exam/treatments related to thepatient and generated by the provider and/or other members of the careteam listed in the care team section 3306, and/or the like. In variousembodiments, the user portal 2610 may provide a user interface viewand/or portlet for generating a report, entering a diagnosis, and/orotherwise providing information/data to be included in the patient's ER(the ER for which the patient is the subject entity).

In various embodiments, user portals 2610 may be provided for a varietyof providers. For example, various user portals 2610 may be providedthat are tailored to the various types of providers. For example, the ERsystem 100 may provide a user portal 2610 tailored to physicians, nursepractitioners, lab workers, case managers, physical therapists, mentalhealth practitioners, and/or the like. For example, FIGS. 34A and 34Bprovide an example “management plan” view portlet 3400 that may beprovided to a case worker via a tailored user portal 2610. In an exampleembodiment, the “management plan” view portlet 3400 comprises a patientinformation section 3402 that provides demographic information/datacorresponding to the patient. For example, the patient informationsection 3402 may include the patient name; birthdate; age; sex, gender,and/or preferred pronouns; and/or the like. The “management plan”portlet view 3400 may also include a “management plan” portlet view 3404that includes a summary of various health goals and plans to achievethose health goals for the patient. The health goals and plans toachieve those health goals may correspond to input and/or actions to betaken by the patient and to one or more providers. Various other userportals 2610 and/or user portlets 2612 that are tailored to variousproviders and/or provider types may be provided in various embodiments.

In various embodiments, the ER system 100 may provide a user portal 2610for use by patients to view at least a portion of the information/datastored in the patient's ER and/or contribute information/data to thepatient's ER. For example, FIG. 35 provides an example view of adashboard and/or landing page 3500 for a patient provided via a userportal 2610. In various embodiments, the dashboard and/or landing page3500 comprises a patient indicator 3502 configured to indicate the userand/or patient that the dashboard and/or landing page 3500 was populatedfor. For example, the dashboard and/or landing page 3500 may comprise apatient management links section 3504, via which a user/patient mayselect a link to submit updates for a corresponding aspect of the ER forwhich the patient is the subject entity. For example, the dashboardand/or landing page 3500 may comprise a “reminders” portlet view 3506configured to provide the user/patient with reminders of any upcomingactions that should be taken on the part of the patient/user. Forexample, the dashboard and/or landing page 3500 may comprise an inboxsection 3508 that may be used to receive and/or send secured messages toone or more providers of the patient's care team. For example, thedashboard and/or landing page 3500 may comprise a “my conditions”portlet view 3510 that lists recent and/or current diagnoses for theuser/patient. For example, the dashboard and/or landing page 3500 maycomprise a “my allergies” portlet view 3512 that lists theuser/patient's known allergies. For example, the dashboard and/orlanding page 3500 may comprise a “my medications” portlet view 3514 thatlists medications the user/patient is taking and/or medicationsprescribed to the user/patient. For example, the dashboard and/orlanding page 3500 may comprise a “my health” indicators portlet view3516 which may provide a list of test and/or lab work results, periodictests and/or exams, and/or the like corresponding to the user/patient.For example, the dashboard and/or landing page 3500 may comprise anupcoming appointment section 3518 that shows upcoming scheduledappointments for the user/patient. In various embodiments, theinformation/data provided in the “reminders” portlet view 3506, “myconditions” portlet view 3510, “my allergies” portlet view 3512, “mymedications” portlet view 3514, “my health indicators” portlet view3516, and/or upcoming appointments section 3518 may be accessed from theER associated with the subject entity identifier corresponding to thepatient and stored in the data storage 211. For example, theinformation/data provided in the “reminders” portlet view 3506, “myconditions” portlet view 3510, “my allergies” portlet view 3512, “mymedications” portlet view 3514, “my health indicators” portlet view3516, and/or upcoming appointments section 3518 may be populated througha process similar to that described with respect to FIG. 29.

In various embodiments, a user may select an element from the dashboardand/or landing page 3500 to view in depth information regarding thatelement. For example, a user/patient may select (e.g., click on) anelement from the “my conditions” portlet view 3510 corresponding to type2 diabetes. Responsive to the user/patient's selection, the user portal2610 may cause a “diabetes” portlet view 3600 to be provided (e.g., viaa user interface, such as display 316, of a user computing entity 30being operated by the user).

In an example embodiment, the “diabetes” portlet view 3600 comprises apatient information section 3602 that provides demographicinformation/data corresponding to the patient. For example, the patientinformation section 3602 may include the patient name; birthdate; age;sex, gender, and/or preferred pronouns; and/or the like. In an exampleembodiment, the “diabetes” portlet view 3600 comprises a managementoverview 3604 which lists various actions and/or health conditions tomonitors as part of managing the user/patient's diabetes and anindication of whether those actions are complete or need to be completedand/or a current status of the monitored health conditions. In anexample embodiment, the “diabetes” portlet view 3600 comprises a visithistory section 3606 indicating recent visits to various providersrelated to the user/patient's diabetes and/or management thereof. In anexample embodiment, the “diabetes” portlet view 3600 comprises an actionplan section 3608 that indicates various actions and/or steps to betaken as part of managing the user/patient's diabetes. The action plansection 3608 may comprise links to detailed information/datacorresponding to one or more of the various actions and/or steps. In anexample embodiment, the “diabetes” portlet view 3600 comprises aresources section 3610 that includes one or more links to variousresources that the user/patient may view regarding the user/patient'shealth condition of diabetes, management thereof, things to expect,disease progression, slowing disease progression, and/or the like. In anexample embodiment, the “diabetes” portlet view 3600 comprises a currentstatus section 3612 that indicates the current status of theuser/patient's blood sugar and/or other indicators corresponding to howwell the user/patient's health condition is being managed. In an exampleembodiment, a user may mouse over or select (e.g., click) an actionlisted on the “diabetes” portlet view 3600 to view detailed instructions3614 for completing the action. For example, the user/patient may mouseover or select (e.g., click) the “record your glucose level in yourblood sugar diary” action and be provided with detailed instructions3614 regarding how to measure the user/patient's glucose level andrecord the result. In various embodiments, the detailed instructions3614 are provided in a pop-up window, an embedded window, a dialog box,and/or the like. The user/patient may select one or more other elementsdisplayed on the “diabetes” portlet view 3600 to access furtherinformation/data regarding the selected element.

In various embodiments, the information/data provided in the patientinformation section 3602, management overview 3604, visit historysection 3606, and/or current status section 3612 may be accessed fromthe ER associated with the subject entity identifier corresponding tothe patient and stored in the data storage 211. For example, theinformation/data provided in the patient information section 3602,management overview 3604, visit history section 3606, and/or currentstatus section 3612 may be populated through a process similar to thatdescribed with respect to FIG. 29. In various embodiments, theinformation/data provided in the action plan section 3608, resourcessection 3610, and/or detailed instructions 3614 are provided based atleast in part on information/data stored in a data store in associationwith one or more ontology concept identifiers and are selected to beprovided via the “diabetes” portlet view 3600 based at least in part onthose one or more ontology concept identifiers.

Various other user portals 2610 and/or user portlets 2612 may beprovided in various embodiments, as appropriate for the application,which provide various users with appropriate information/data for theuser's role in the ER system 100 and that takes advantage of the domainontology of the ER system 100.

V. CONCLUSION

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

1. A method for ingesting an incoming message, the method comprising:receiving, by a computing entity, an observable packet data object or adata artifact packet data object, wherein (a) the data artifact packetdata object is generated based at least in part on the observable packetdata object, (b) the observable packet data object is an XML documentgenerated based at least in part on parsing a message received from asource system, (c) the data artifact packet data object comprises anentity identifier identifying a subject entity and a pluralityobservable-value pairs, and (d) each observable in a correspondingobservable-value pair is identifiable by an ontology concept identifierdefined by a graph-based domain ontology; automatically generating, bythe computing entity, a container tree data structure comprising a dataartifact packet container node as a root node, wherein (a) the containertree data structure is generated based at least in part on the dataartifact packet data object, (b) the container tree data structurecomprises a plurality of container nodes that are descendants of theroot node based at least in part on the data artifact packet dataobject, and (c) each container node of the plurality of container nodescorresponds to one pair of the plurality of observable-value pairs;automatically identifying, by the computing entity, an entity associatedwith the container tree data structure based at least in part on theentity identifier; automatically traversing, by the computing entity,the container tree data structure in a depth-first traversal to generatea data transfer object for each of container node of the plurality ofcontainer nodes, wherein each data transfer object corresponds to onepair of the plurality of observable-value pairs; and automaticallyproviding, by the computing entity, at least one of the plurality ofdata transfer objects for use in performing a database update function.2. The method of claim 1, wherein a value of at least oneobservable-value pair of the plurality observable-value pairs comprisesat least one of a source vocabulary description, a source vocabularycode, or a text description of an observable.
 3. The method of claim 2,wherein, during the depth-first traversal of the container tree datastructure, an aggregator method resolves the at least one of the sourcevocabulary description, the source vocabulary code, or the textdescription of the observable.
 4. The method of claim 1, wherein ahierarchy of container nodes of the container tree is determined basedat least in part on the graph-based domain ontology.
 5. The method ofclaim 1, wherein automatically generating the container tree datastructure comprises: determining a type of container node for anobservable-value pair of the plurality of observable-value pairs basedat least in part on the graph-based domain ontology; determining whethera container node having the determined type is present in the containertree data structure; and responsive to determining that a container nodehaving the determined type is present in the container tree datastructure, storing the observable-value pair in the container node. 6.The method of claim 1, wherein automatically generating the containertree data structure comprises: determining a type of container node foran observable-value pair of the plurality of observable-value pairsbased at least in part on the graph-based domain ontology; determiningwhether a container node having the determined type is present in thecontainer tree data structure; and responsive to determining that acontainer node having the determined type is not present in thecontainer tree data structure: (a) constructing the container nodehaving the determine type, (b) inserting the container node into anappropriate position in the container tree data structure, wherein theappropriate position in the container tree data structure is determinedbased at least in part on the graph-based domain ontology, and (c)storing the observable-value pair in the container node.
 7. The methodof claim 1, wherein the depth-first traversal of the container tree datastructure comprises comparing a value of an observable-value pair of theplurality of observable-value pairs to at least one requirementcorresponding to an observable of the observable-value pair to determinewhether the value satisfies the at least one requirement.
 8. The methodof claim 1 further comprising determining a confidence score for each ofthe plurality of observable-value pairs.
 9. A system comprising one ormore processors, one or more memory storage areas comprising programcode, the one or more memory storage areas and the program codeconfigured to, with the one or more processors, cause the system to atleast: receive an observable packet data object or a data artifactpacket data object, wherein (a) the data artifact packet data object isgenerated based at least in part on the observable packet data object,(b) the observable packet data object is an XML document generated basedat least in part on parsing a message received from a source system, (c)the data artifact packet data object comprises an entity identifieridentifying a subject entity and a plurality observable-value pairs, and(d) each observable in a corresponding observable-value pair isidentifiable by an ontology concept identifier defined by a graph-baseddomain ontology; automatically generate a container tree data structurecomprising a data artifact packet container node as a root node, wherein(a) the container tree data structure is generated based at least inpart on the data artifact packet data object, (b) the container treedata structure comprises a plurality of container nodes that aredescendants of the root node based at least in part on the data artifactpacket data object, and (c) each container node of the plurality ofcontainer nodes corresponds to one pair of the plurality ofobservable-value pairs; automatically identify an entity associated withthe container tree data structure based at least in part on the entityidentifier; automatically traverse the container tree data structure ina depth-first traversal to generate a data transfer object for each ofcontainer node of the plurality of container nodes, wherein each datatransfer object corresponds to one pair of the plurality ofobservable-value pairs; and automatically provide at least one of theplurality of data transfer objects for use in performing a databaseupdate function.
 10. The system of claim 9, wherein a value of at leastone observable-value pair of the plurality observable-value pairscomprises at least one of a source vocabulary description, a sourcevocabulary code, or a text description of an observable.
 11. The systemof claim 10, wherein, during the depth-first traversal of the containertree data structure, an aggregator method resolves the at least one ofthe source vocabulary description, the source vocabulary code, or thetext description of the observable.
 12. The system of claim 9, wherein ahierarchy of container nodes of the container tree is determined basedat least in part on the graph-based domain ontology.
 13. The system ofclaim 9, wherein automatically generating the container tree datastructure comprises: determining a type of container node for anobservable-value pair of the plurality of observable-value pairs basedat least in part on the graph-based domain ontology; determining whethera container node having the determined type is present in the containertree data structure; and responsive to determining that a container nodehaving the determined type is present in the container tree datastructure, storing the observable-value pair in the container node. 14.The system of claim 9, wherein automatically generating the containertree data structure comprises: determining a type of container node foran observable-value pair of the plurality of observable-value pairsbased at least in part on the graph-based domain ontology; determiningwhether a container node having the determined type is present in thecontainer tree data structure; and responsive to determining that acontainer node having the determined type is not present in thecontainer tree data structure: (a) constructing the container nodehaving the determine type, (b) inserting the container node into anappropriate position in the container tree data structure, wherein theappropriate position in the container tree data structure is determinedbased at least in part on the graph-based domain ontology, and (c)storing the observable-value pair in the container node.
 15. The systemof claim 9, wherein the depth-first traversal of the container tree datastructure comprises comparing a value of an observable-value pair of theplurality of observable-value pairs to at least one requirementcorresponding to an observable of the observable-value pair to determinewhether the value satisfies the at least one requirement.
 16. The systemof claim 9, wherein the one or more memory storage areas and the programcode are further configured to, with the one or more processors, causethe system to at least determine a confidence score for each of theplurality of observable-value pairs.
 17. A computer program productcomprising at least one non-transitory computer-readable storage mediumhaving computer-readable program code stored therein, thecomputer-readable program code configured to at least: receive anobservable packet data object or a data artifact packet data object,wherein (a) the data artifact packet data object is generated based atleast in part on the observable packet data object, (b) the observablepacket data object is an XML document generated based at least in parton parsing a message received from a source system, (c) the dataartifact packet data object comprises an entity identifier identifying asubject entity and a plurality observable-value pairs, and (d) eachobservable in a corresponding observable-value pair is identifiable byan ontology concept identifier defined by a graph-based domain ontology;automatically generate a container tree data structure comprising a dataartifact packet container node as a root node, wherein (a) the containertree data structure is generated based at least in part on the dataartifact packet data object, (b) the container tree data structurecomprises a plurality of container nodes that are descendants of theroot node based at least in part on the data artifact packet dataobject, and (c) each container node of the plurality of container nodescorresponds to one pair of the plurality of observable-value pairs;automatically identify an entity associated with the container tree datastructure based at least in part on the entity identifier; automaticallytraverse the container tree data structure in a depth-first traversal togenerate a data transfer object for each of container node of theplurality of container nodes, wherein each data transfer objectcorresponds to one pair of the plurality of observable-value pairs; andautomatically provide at least one of the plurality of data transferobjects for use in performing a database update function.
 18. Thecomputer program product of claim 17, wherein a value of at least oneobservable-value pair of the plurality observable-value pairs comprisesat least one of a source vocabulary description, a source vocabularycode, or a text description of an observable.
 19. The computer programproduct of claim 18, wherein, during the depth-first traversal of thecontainer tree data structure, an aggregator method resolves the atleast one of the source vocabulary description, the source vocabularycode, or the text description of the observable.
 20. The computerprogram product of claim 17, wherein a hierarchy of container nodes ofthe container tree is determined based at least in part on thegraph-based domain ontology.
 21. The computer program product of claim17, wherein automatically generating the container tree data structurecomprises: determining a type of container node for an observable-valuepair of the plurality of observable-value pairs based at least in parton the graph-based domain ontology; determining whether a container nodehaving the determined type is present in the container tree datastructure; and responsive to determining that a container node havingthe determined type is present in the container tree data structure,storing the observable-value pair in the container node.
 22. Thecomputer program product of claim 17, wherein automatically generatingthe container tree data structure comprises: determining a type ofcontainer node for an observable-value pair of the plurality ofobservable-value pairs based at least in part on the graph-based domainontology; determining whether a container node having the determinedtype is present in the container tree data structure; and responsive todetermining that a container node having the determined type is notpresent in the container tree data structure: (a) constructing thecontainer node having the determine type, (b) inserting the containernode into an appropriate position in the container tree data structure,wherein the appropriate position in the container tree data structure isdetermined based at least in part on the graph-based domain ontology,and (c) storing the observable-value pair in the container node.
 23. Thecomputer program product of claim 17, wherein the depth-first traversalof the container tree data structure comprises comparing a value of anobservable-value pair of the plurality of observable-value pairs to atleast one requirement corresponding to an observable of theobservable-value pair to determine whether the value satisfies the atleast one requirement.
 24. The computer program product of claim 17,wherein the one or more memory storage areas and the program code arefurther configured to, with the one or more processors, cause the systemto at least determine a confidence score for each of the plurality ofobservable-value pairs.